IEEE PROJECTS 2016 - 2017
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Project Domain list 2016
1. IEEE based on datamining and knowledge engineering,
2. IEEE based on mobile computing,
3. IEEE based on networking,
4. IEEE based on Image processing,
5. IEEE based on Multimedia,
6. IEEE based on Network security,
7. IEEE based on parallel and distributed systems
Project Domain list 2016
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2016
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
5. IOT Projects
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US:-
1 CRORE PROJECTS
Door No: 66 ,Ground Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 7708150152
This document summarizes a research paper that presents a reputation system called Tulungan designed to measure contributor and rater reputation in collaborative web filtering systems. Tulungan aims to be resistant to slandering attacks, where malicious raters intentionally give inaccurate negative ratings. The paper describes Tulungan's algorithm, which initializes reputations and allows contributions, rating, and reputation computation in cycles. A simulation evaluates Tulungan in the presence of good, malicious, and slandering users, finding that Tulungan is effective even when good users are a minority and is resistant to slandering.
TULUNGAN: A SLANDERING-RESISTANT REPUTATION SYSTEM FOR COLLABORATIVE WEB FILT...IJNSA Journal
Reputation systems measure the credibility of contributions and contributors in collaborative web systems. Measuring the credibility is significant since a collaborative environment generally allows anyone with Internet access to provide contribution.
Collaborative web systems are susceptible to malicious users who intentionally provide inaccurate contents. With the help of reputation systems, the effect of such malicious activities can be reduced. Reputation systems allow Internet users to rate the contributions made by other users. However, there are malicious users who will go beyond providing wrong contributions. They attempt to make reputation systems useless by launching attacks such as slandering. Slandering happens when a malicious user or group of malicious users intentionally provide a negative rating to accurate contributions provided by good users. Such activity lowers the reputation of good users and in most cases it even helps improve the reputation of slandering users.
This paper presents a reputation system called Tulungan that is designed to measure the contributor and rater reputation of users of a collaborative web system that is used for web filtering. User contributions are in the form of URL categorizations. It is the role of Tulungan to determine the correctness of the categorizations. A simulation is presented to validate the resilience of Tulungan in the presence of slandering users. The result of the simulation shows that Tulungan is not only resistant to slandering but it is still effective even if the number of good users is less than its slandering counterpart. Even if there are only 15% good users, the number of correct URL categorizations outnumbers incorrect contributions.
An iac approach for detecting profile cloningIJNSA Journal
Nowadays, Online Social Networks (OSNs) are popular websites on the internet, which millions of users
register on and share their own personal information with others. Privacy threats and disclosing personal
information are the most important concerns of OSNs’ users. Recently, a new attack which is named
Identity Cloned Attack is detected on OSNs. In this attack the attacker tries to make a fake identity of a real
user in order to access to private information of the users’ friends which they do not publish on the public
profiles. In today OSNs, there are some verification services, but they are not active services and they are
useful for users who are familiar with online identity issues. In this paper, Identity cloned attacks are
explained in more details and a new and precise method to detect profile cloning in online social networks
is proposed. In this method, first, the social network is shown in a form of graph, then, according to
similarities among users, this graph is divided into smaller communities. Afterwards, all of the similar
profiles to the real profile are gathered (from the same community), then strength of relationship (among
all selected profiles and the real profile) is calculated, and those which have the less strength of
relationship will be verified by mutual friend system. In this study, in order to evaluate the effectiveness of
proposed method, all steps are applied on a dataset of Facebook, and finally this work is compared with
two previous works by applying them on the dataset.
1. The document discusses issues around detecting spam and identifying fake profiles on social networks like Facebook, Twitter, and Sina Weibo.
2. It notes that 20-40% of profiles on social networks could be fake, and identifies challenges in differentiating fake from legitimate profiles due to limited publicly available user data and varying privacy policies across networks.
3. The paper aims to identify approaches for detecting fake profiles using only the minimal publicly available data on each network, and to evaluate their accuracy compared to existing methods using more extensive data.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
There are many malicious programs disbursing on Face book every single day. Within the recent occasions, online hackers have thought about recognition within the third-party application platform additionally to deployment of malicious programs. Programs that present appropriate method of online hackers to spread malicious content on Face book however, little is known concerning highlights of malicious programs and just how they function. Our goal ought to be to create a comprehensive application evaluator of face book the very first tool that will depend on recognition of malicious programs on Face book. To develop rigorous application evaluator of face book we utilize information that's collected by way of observation of posting conduct of Face book apps that are seen across numerous face book clients. This can be frequently possibly initial comprehensive study which has dedicated to malicious Face book programs that concentrate on quantifying additionally to knowledge of malicious programs making these particulars in to a effective recognition method. For structuring of rigorous application evaluator of face book, we utilize data within the security application within Facebook that examines profiles of Facebook clients.
Presentation-Detecting Spammers on Social NetworksAshish Arora
This document summarizes research on detecting spammers on social networks. The researchers created fake "honeypot" profiles on Facebook, Twitter, and MySpace to collect data from spammers over 12 months. They analyzed the data to identify patterns in spam bots and campaigns. Machine learning techniques were used to develop features that detected spammers with 2-3% error rates. The techniques help social networks improve security and identify malicious users spreading spam.
This document summarizes a research paper that presents a reputation system called Tulungan designed to measure contributor and rater reputation in collaborative web filtering systems. Tulungan aims to be resistant to slandering attacks, where malicious raters intentionally give inaccurate negative ratings. The paper describes Tulungan's algorithm, which initializes reputations and allows contributions, rating, and reputation computation in cycles. A simulation evaluates Tulungan in the presence of good, malicious, and slandering users, finding that Tulungan is effective even when good users are a minority and is resistant to slandering.
TULUNGAN: A SLANDERING-RESISTANT REPUTATION SYSTEM FOR COLLABORATIVE WEB FILT...IJNSA Journal
Reputation systems measure the credibility of contributions and contributors in collaborative web systems. Measuring the credibility is significant since a collaborative environment generally allows anyone with Internet access to provide contribution.
Collaborative web systems are susceptible to malicious users who intentionally provide inaccurate contents. With the help of reputation systems, the effect of such malicious activities can be reduced. Reputation systems allow Internet users to rate the contributions made by other users. However, there are malicious users who will go beyond providing wrong contributions. They attempt to make reputation systems useless by launching attacks such as slandering. Slandering happens when a malicious user or group of malicious users intentionally provide a negative rating to accurate contributions provided by good users. Such activity lowers the reputation of good users and in most cases it even helps improve the reputation of slandering users.
This paper presents a reputation system called Tulungan that is designed to measure the contributor and rater reputation of users of a collaborative web system that is used for web filtering. User contributions are in the form of URL categorizations. It is the role of Tulungan to determine the correctness of the categorizations. A simulation is presented to validate the resilience of Tulungan in the presence of slandering users. The result of the simulation shows that Tulungan is not only resistant to slandering but it is still effective even if the number of good users is less than its slandering counterpart. Even if there are only 15% good users, the number of correct URL categorizations outnumbers incorrect contributions.
An iac approach for detecting profile cloningIJNSA Journal
Nowadays, Online Social Networks (OSNs) are popular websites on the internet, which millions of users
register on and share their own personal information with others. Privacy threats and disclosing personal
information are the most important concerns of OSNs’ users. Recently, a new attack which is named
Identity Cloned Attack is detected on OSNs. In this attack the attacker tries to make a fake identity of a real
user in order to access to private information of the users’ friends which they do not publish on the public
profiles. In today OSNs, there are some verification services, but they are not active services and they are
useful for users who are familiar with online identity issues. In this paper, Identity cloned attacks are
explained in more details and a new and precise method to detect profile cloning in online social networks
is proposed. In this method, first, the social network is shown in a form of graph, then, according to
similarities among users, this graph is divided into smaller communities. Afterwards, all of the similar
profiles to the real profile are gathered (from the same community), then strength of relationship (among
all selected profiles and the real profile) is calculated, and those which have the less strength of
relationship will be verified by mutual friend system. In this study, in order to evaluate the effectiveness of
proposed method, all steps are applied on a dataset of Facebook, and finally this work is compared with
two previous works by applying them on the dataset.
1. The document discusses issues around detecting spam and identifying fake profiles on social networks like Facebook, Twitter, and Sina Weibo.
2. It notes that 20-40% of profiles on social networks could be fake, and identifies challenges in differentiating fake from legitimate profiles due to limited publicly available user data and varying privacy policies across networks.
3. The paper aims to identify approaches for detecting fake profiles using only the minimal publicly available data on each network, and to evaluate their accuracy compared to existing methods using more extensive data.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
There are many malicious programs disbursing on Face book every single day. Within the recent occasions, online hackers have thought about recognition within the third-party application platform additionally to deployment of malicious programs. Programs that present appropriate method of online hackers to spread malicious content on Face book however, little is known concerning highlights of malicious programs and just how they function. Our goal ought to be to create a comprehensive application evaluator of face book the very first tool that will depend on recognition of malicious programs on Face book. To develop rigorous application evaluator of face book we utilize information that's collected by way of observation of posting conduct of Face book apps that are seen across numerous face book clients. This can be frequently possibly initial comprehensive study which has dedicated to malicious Face book programs that concentrate on quantifying additionally to knowledge of malicious programs making these particulars in to a effective recognition method. For structuring of rigorous application evaluator of face book, we utilize data within the security application within Facebook that examines profiles of Facebook clients.
Presentation-Detecting Spammers on Social NetworksAshish Arora
This document summarizes research on detecting spammers on social networks. The researchers created fake "honeypot" profiles on Facebook, Twitter, and MySpace to collect data from spammers over 12 months. They analyzed the data to identify patterns in spam bots and campaigns. Machine learning techniques were used to develop features that detected spammers with 2-3% error rates. The techniques help social networks improve security and identify malicious users spreading spam.
Sampling of User Behavior Using Online Social NetworkEditor IJCATR
The popularity of online networks provides an opportunity to study the characteristics of online social network graphs is important, both to improve current systems and to design new application of online social networks. Although personalized search has been proposed for many years and many personalization strategies have been investigated, it is still unclear whether personalization is consistently effective on different queries for different users, and under different search contexts. In this paper, we study performance of information collection in a dynamic social network. By analyzing the results, we reveal that personalized search has significant improvement over common web search.
The mixing time of thee sampling process strongly depends on the characteristics of the graph.
Seminar on detecting fake accounts in social media using machine learningParvathi Sanil Nair
The document discusses research on detecting fake identities using machine learning on social media platforms. It begins with an abstract stating that little research has been done to detect fake human accounts specifically. The research applies machine learning models to attributes of fake accounts in hopes of advancing detection. It then outlines the contents which include introduction, related work on spam detection, research on detecting fake identities, research results, and conclusions. Key points are that identity deception is a major problem on social media, and while models can detect bot accounts, they are less successful at detecting fake human accounts using only profile attributes without behavioral data. The random forest model achieved the best results with an F1 score of 49.75% for detecting fake human accounts.
This document discusses distinguishing human users from bot users in web search logs. It proposes using multiple thresholds for different classification criteria rather than single thresholds, to avoid misclassifying ambiguous cases. It also defines "strong criteria" that identify activity levels unlikely or impossible for humans, to avoid false positives. The authors apply this approach to the AOL search log to classify over 92% of users as human and 0.6% as bots, with the rest unclassified. Humans tend to display consistent behavior while bots can vary widely between criteria.
Authorization mechanism for multiparty data sharing in social networkeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Multi-Mode Conceptual Clustering Algorithm Based Social Group Identification ...inventionjournals
The problem of web search time complexity and accuracy has been visited in many research papers, and the authors discussed many approaches to improve the search performance. Still the approaches does not produce any noticeable improvement and struggles with more time complexity as well. To overcome the issues identified, an efficient multi mode conceptual clustering algorithm has been discussed in this paper, which identifies the similar interested user groups by clustering their search context according to different conceptual queries. Identified user groups are shared with the related conceptual queries and their results to reduce the time complexity. The multi mode conceptual clustering, performs grouping of search queries and users according to number of users and their search pattern. The concept of search is identified by using Natural language processing methods and the web logs produced by the default web search engines. The author designed a dedicated web interface to collect the web log about the user search and the same data has been used to cluster the social groups according to number of conceptual queries. The search results has been shared between the users of identified social groups which reduces the search time complexity and improves the efficiency of web search in better manner
Friend Recommendation on Social Network Site Based on Their Life Stylepaperpublications3
Abstract: Social network sites attracted millions of users. In the social network sites, a user can register other users as friends and enjoy communication. Existing social networking sites recommend friends to users based on their social graphs, which may not be appropriate. In proposed system friends recommends to users based on their life styles instead of social graphs. It done by means of sensor rich smart- phone serve as the ideal platform for sensing daily routines from which people’s life styles could be discovered. Unsupervised learning method is used. Achieve an efficient activity Recognition and reduce the false positive of Friend Recommendation. Friendbook integrates a feedback mechanism. Finally the results show that the recommendations accurately reflect the preferences of users in choosing friends.
The document describes a project to detect fake news using machine learning models. It discusses how the project classified news websites as real or fake using a combination of bag-of-words, word embeddings and feature descriptions with 87.39% accuracy. Some ways to improve the model are also provided, such as using more features in the word embeddings. Real-world applications of fake news detection include verifying news on social media during elections and detecting fake job postings.
3 iaetsd semantic web page recommender systemIaetsd Iaetsd
This document discusses a semantic web-page recommender system that aims to improve upon traditional recommender systems. It proposes two novel knowledge representation models: 1) a semantic network that represents domain knowledge through terms, webpages, and relationships automatically constructed from website data, and 2) a conceptual prediction model that integrates semantic knowledge with web usage data to create a weighted semantic network for making recommendations. The system seeks to overcome limitations of prior systems by automating knowledge base construction and addressing the "new-item problem" through incorporation of semantic information. Evaluation shows the proposed approach yields better performance than existing web usage-based recommender systems.
This document discusses methods for collecting reliable data on user behavior from online social networks. It reviews previous research that has collected social network data through OSN APIs/crawling, surveys/questionnaires, and custom application deployment. The authors argue that these methods can produce biased data as they often only collect publicly shared content or rely on self-reported information. The authors propose combining existing methods, specifically the Experience Sampling Method, to collect more reliable data by capturing both shared and non-shared user behaviors and contexts through in situ experiments.
Friendbook a semantic based friend recommendation system for social networksPapitha Velumani
This document describes Friendbook, a semantic-based friend recommendation system that analyzes users' lifestyle data collected from sensors in smartphones to recommend potential friends. It aims to address limitations of existing social networks that rely only on users' social graphs. Friendbook uses topic modeling to extract lifestyle information from daily sensor data and measures similarity between users' lifestyles to identify likely friend candidates. The system was implemented on Android smartphones and evaluated through experiments.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Friendbook is a semantic-based friend recommendation system for social networks that recommends friends based on users' lifestyles rather than social graphs. It uses sensors in smartphones to discover users' lifestyles from daily activities and measures lifestyle similarity between users. Users are recommended as friends if their lifestyles are highly similar. Lifestyles are extracted from "life documents" of daily activities using Latent Dirichlet Allocation. Friendbook also incorporates feedback to improve recommendation accuracy. It was implemented on Android smartphones and evaluated on small and large-scale tests, finding recommendations accurately reflected real-life friend preferences.
The document describes AB Serigrafi, a Turkish printing and label company. It details the company's growth from a 150 sqm space to owning a 3,000 sqm production building. The company aims to be a leader in quality printing and labels through innovation. Its mission is to create innovative, high-quality products on time to satisfy customers while following developments in the industry. All employees are committed to ensuring the highest quality standards. The company offers various printing processes and machinery and produces innovative products like long-lasting labels in different shapes, colors and materials.
Latest 2016 IEEE Projects | 2016 Final Year Project Titles - 1 Crore Projects1crore projects
IEEE PROJECTS 2016 - 2017
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Project Domain list 2016
1. IEEE based on datamining and knowledge engineering,
2. IEEE based on mobile computing,
3. IEEE based on networking,
4. IEEE based on Image processing,
5. IEEE based on Multimedia,
6. IEEE based on Network security,
7. IEEE based on parallel and distributed systems
Project Domain list 2016
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2016
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
5. IOT Projects
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US:-
1 CRORE PROJECTS
Door No: 66 ,Ground Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 7708150152
IEEE PROJECTS 2016 - 2017
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Project Domain list 2016
1. IEEE based on datamining and knowledge engineering,
2. IEEE based on mobile computing,
3. IEEE based on networking,
4. IEEE based on Image processing,
5. IEEE based on Multimedia,
6. IEEE based on Network security,
7. IEEE based on parallel and distributed systems
Project Domain list 2016
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2016
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
5. IOT Projects
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US:-
1 CRORE PROJECTS
Door No: 66 ,Ground Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 7708150152
Sampling of User Behavior Using Online Social NetworkEditor IJCATR
The popularity of online networks provides an opportunity to study the characteristics of online social network graphs is important, both to improve current systems and to design new application of online social networks. Although personalized search has been proposed for many years and many personalization strategies have been investigated, it is still unclear whether personalization is consistently effective on different queries for different users, and under different search contexts. In this paper, we study performance of information collection in a dynamic social network. By analyzing the results, we reveal that personalized search has significant improvement over common web search.
The mixing time of thee sampling process strongly depends on the characteristics of the graph.
Seminar on detecting fake accounts in social media using machine learningParvathi Sanil Nair
The document discusses research on detecting fake identities using machine learning on social media platforms. It begins with an abstract stating that little research has been done to detect fake human accounts specifically. The research applies machine learning models to attributes of fake accounts in hopes of advancing detection. It then outlines the contents which include introduction, related work on spam detection, research on detecting fake identities, research results, and conclusions. Key points are that identity deception is a major problem on social media, and while models can detect bot accounts, they are less successful at detecting fake human accounts using only profile attributes without behavioral data. The random forest model achieved the best results with an F1 score of 49.75% for detecting fake human accounts.
This document discusses distinguishing human users from bot users in web search logs. It proposes using multiple thresholds for different classification criteria rather than single thresholds, to avoid misclassifying ambiguous cases. It also defines "strong criteria" that identify activity levels unlikely or impossible for humans, to avoid false positives. The authors apply this approach to the AOL search log to classify over 92% of users as human and 0.6% as bots, with the rest unclassified. Humans tend to display consistent behavior while bots can vary widely between criteria.
Authorization mechanism for multiparty data sharing in social networkeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Multi-Mode Conceptual Clustering Algorithm Based Social Group Identification ...inventionjournals
The problem of web search time complexity and accuracy has been visited in many research papers, and the authors discussed many approaches to improve the search performance. Still the approaches does not produce any noticeable improvement and struggles with more time complexity as well. To overcome the issues identified, an efficient multi mode conceptual clustering algorithm has been discussed in this paper, which identifies the similar interested user groups by clustering their search context according to different conceptual queries. Identified user groups are shared with the related conceptual queries and their results to reduce the time complexity. The multi mode conceptual clustering, performs grouping of search queries and users according to number of users and their search pattern. The concept of search is identified by using Natural language processing methods and the web logs produced by the default web search engines. The author designed a dedicated web interface to collect the web log about the user search and the same data has been used to cluster the social groups according to number of conceptual queries. The search results has been shared between the users of identified social groups which reduces the search time complexity and improves the efficiency of web search in better manner
Friend Recommendation on Social Network Site Based on Their Life Stylepaperpublications3
Abstract: Social network sites attracted millions of users. In the social network sites, a user can register other users as friends and enjoy communication. Existing social networking sites recommend friends to users based on their social graphs, which may not be appropriate. In proposed system friends recommends to users based on their life styles instead of social graphs. It done by means of sensor rich smart- phone serve as the ideal platform for sensing daily routines from which people’s life styles could be discovered. Unsupervised learning method is used. Achieve an efficient activity Recognition and reduce the false positive of Friend Recommendation. Friendbook integrates a feedback mechanism. Finally the results show that the recommendations accurately reflect the preferences of users in choosing friends.
The document describes a project to detect fake news using machine learning models. It discusses how the project classified news websites as real or fake using a combination of bag-of-words, word embeddings and feature descriptions with 87.39% accuracy. Some ways to improve the model are also provided, such as using more features in the word embeddings. Real-world applications of fake news detection include verifying news on social media during elections and detecting fake job postings.
3 iaetsd semantic web page recommender systemIaetsd Iaetsd
This document discusses a semantic web-page recommender system that aims to improve upon traditional recommender systems. It proposes two novel knowledge representation models: 1) a semantic network that represents domain knowledge through terms, webpages, and relationships automatically constructed from website data, and 2) a conceptual prediction model that integrates semantic knowledge with web usage data to create a weighted semantic network for making recommendations. The system seeks to overcome limitations of prior systems by automating knowledge base construction and addressing the "new-item problem" through incorporation of semantic information. Evaluation shows the proposed approach yields better performance than existing web usage-based recommender systems.
This document discusses methods for collecting reliable data on user behavior from online social networks. It reviews previous research that has collected social network data through OSN APIs/crawling, surveys/questionnaires, and custom application deployment. The authors argue that these methods can produce biased data as they often only collect publicly shared content or rely on self-reported information. The authors propose combining existing methods, specifically the Experience Sampling Method, to collect more reliable data by capturing both shared and non-shared user behaviors and contexts through in situ experiments.
Friendbook a semantic based friend recommendation system for social networksPapitha Velumani
This document describes Friendbook, a semantic-based friend recommendation system that analyzes users' lifestyle data collected from sensors in smartphones to recommend potential friends. It aims to address limitations of existing social networks that rely only on users' social graphs. Friendbook uses topic modeling to extract lifestyle information from daily sensor data and measures similarity between users' lifestyles to identify likely friend candidates. The system was implemented on Android smartphones and evaluated through experiments.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Friendbook is a semantic-based friend recommendation system for social networks that recommends friends based on users' lifestyles rather than social graphs. It uses sensors in smartphones to discover users' lifestyles from daily activities and measures lifestyle similarity between users. Users are recommended as friends if their lifestyles are highly similar. Lifestyles are extracted from "life documents" of daily activities using Latent Dirichlet Allocation. Friendbook also incorporates feedback to improve recommendation accuracy. It was implemented on Android smartphones and evaluated on small and large-scale tests, finding recommendations accurately reflected real-life friend preferences.
The document describes AB Serigrafi, a Turkish printing and label company. It details the company's growth from a 150 sqm space to owning a 3,000 sqm production building. The company aims to be a leader in quality printing and labels through innovation. Its mission is to create innovative, high-quality products on time to satisfy customers while following developments in the industry. All employees are committed to ensuring the highest quality standards. The company offers various printing processes and machinery and produces innovative products like long-lasting labels in different shapes, colors and materials.
Latest 2016 IEEE Projects | 2016 Final Year Project Titles - 1 Crore Projects1crore projects
IEEE PROJECTS 2016 - 2017
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Project Domain list 2016
1. IEEE based on datamining and knowledge engineering,
2. IEEE based on mobile computing,
3. IEEE based on networking,
4. IEEE based on Image processing,
5. IEEE based on Multimedia,
6. IEEE based on Network security,
7. IEEE based on parallel and distributed systems
Project Domain list 2016
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2016
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
5. IOT Projects
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US:-
1 CRORE PROJECTS
Door No: 66 ,Ground Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 7708150152
IEEE PROJECTS 2016 - 2017
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Project Domain list 2016
1. IEEE based on datamining and knowledge engineering,
2. IEEE based on mobile computing,
3. IEEE based on networking,
4. IEEE based on Image processing,
5. IEEE based on Multimedia,
6. IEEE based on Network security,
7. IEEE based on parallel and distributed systems
Project Domain list 2016
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2016
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
5. IOT Projects
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US:-
1 CRORE PROJECTS
Door No: 66 ,Ground Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 7708150152
2016 BE Final year Projects in chennai - 1 Crore Projects 1crore projects
IEEE PROJECTS 2016 - 2017
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Project Domain list 2016
1. IEEE based on datamining and knowledge engineering,
2. IEEE based on mobile computing,
3. IEEE based on networking,
4. IEEE based on Image processing,
5. IEEE based on Multimedia,
6. IEEE based on Network security,
7. IEEE based on parallel and distributed systems
Project Domain list 2016
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2016
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
5. IOT Projects
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US:-
1 CRORE PROJECTS
Door No: 66 ,Ground Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 7708150152
Makalah ini membahas tentang deteksi dini pada infeksi masa nifas. Infeksi masa nifas adalah masuknya bakteri pada traktus genitalia wanita setelah melahirkan yang ditandai dengan demam lebih dari 38 derajat selama dua hari pertama pasca persalinan. Infeksi ini disebabkan oleh berbagai bakteri seperti Streptococcus, Staphylococcus, dan Escherichia coli yang masuk melalui luka persalinan atau manipulasi medis
Dokumen ini memberikan informasi tentang asuhan bayi usia 2-6 hari. Termasuk pentingnya ASI eksklusif, pola defekasi dan berkemih yang normal pada usia tersebut, serta pola tidur rata-rata bayi yang masih sangat banyak yaitu sekitar 16 jam per hari.
Retrieving Hidden Friends a Collusion Privacy Attack against Online Friend Se...ijtsrd
Online Social Networks OSNs are providing a diversity of application for human users to network through families, friends and even strangers. One of such application, friend search engine, allows the universal public to inquiry individual client friend lists and has been gaining popularity recently. Proper design, this application may incorrectly disclose client private relationship information. Existing work has a privacy perpetuation clarification that can effectively boost OSNs' sociability while protecting users' friendship privacy against attacks launched by individual malicious requestors. In this project proposed an advanced collusion attack, where a victim user's friendship privacy can be compromise from side to side a series of cautiously designed queries coordinately launched by multiple malicious requestors. The result of the proposed collusion attack is validate through synthetic and real world social network data sets. The project on the advanced collusion attacks will help us design a more vigorous and securer friend search engine on OSNs in the near future. R. Brintha | H. Parveen Bagum "Retrieving Hidden Friends a Collusion Privacy Attack against Online Friend Search Engine" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31687.pdf Paper Url :https://www.ijtsrd.com/computer-science/world-wide-web/31687/retrieving-hidden-friends-a-collusion-privacy-attack-against-online-friend-search-engine/r-brintha
Stabilization of Black Cotton Soil with Red Mud and Formulation of Linear Reg...IRJET Journal
This document describes a proposed friend discovery system for online social networks that recommends friends to users based on their lifestyles, behaviors, ratings, profile analyses, and comments rather than just location. It uses a predefined form for users to indicate their daily activities to better determine lifestyle similarities. The system also provides security using AES encryption algorithms. The proposed system aims to address limitations of existing systems that rely only on social graphs or unstructured lifestyle data from users.
Asymmetric Social Proximity Based Private Matching Protocols for Online Socia...1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
BullyNet is a three-phase algorithm that detects cyberbullies on Twitter. It first constructs a cyberbullying signed network (SN) by analyzing tweets and their contexts to determine sentiment and assign bullying scores to connections between users. It then proposes a centrality measure called attitude and merit (A&M) to identify cyberbullies from the SN. The algorithm was tested on a dataset of 5.6 million tweets and effectively detected cyberbullies with high accuracy while scaling to large numbers of tweets.
Trust Metrics In Recommender System : A Surveyaciijournal
This document summarizes trust metrics used in trust-based recommender systems. It discusses four main steps in the trust-based recommendation process: 1) measuring trust between users, 2) selecting neighbors, 3) aggregating neighbors' preferences, and 4) presenting recommendations. It then reviews several influential trust-based recommendation approaches and their trust metrics, including Advogato, Appleseed, TidalTrust, and SUNNY. The metrics calculate trust through propagation along paths in a trust graph based on factors like distance, edge weights, and explicit trust values.
Social media platforms allow for widespread sharing of information but also enable the spread of criminal and unethical ideas. This document proposes a social media analysis tool to detect crime and suspicious profiles on platforms like Twitter. The tool would extract tweet data using APIs, analyze features to identify concerning behaviors, perform topic modeling to detect tweets related to crime, and suggest profiles for suspension. The goal is to prevent the spread of criminal content and activities online through monitoring social media data and flagging problematic accounts.
TRUST METRICS IN RECOMMENDER SYSTEMS: A SURVEYaciijournal
Information overload is a new challenge in e-commerce sites. The problem refers to the fast growing of
information that lead following the information flow in real world be impossible. Recommender systems, as
the most successful application of information filtering, help users to find items of their interest from huge
datasets. Collaborative filtering, as the most successful technique for recommendation, utilises social
behaviours of users to detect their interests. Traditional challenges of Collaborative filtering, such as cold
start, sparcity problem, accuracy and malicious attacks, derived researchers to use new metadata to
improve accuracy of recommenders and solve the traditional problems. Trust based recommender systems
focus on trustworthy value on relation among users to make more reliable and accurate recommends. In
this paper our focus is on trust based approach and discuss about the process of making recommendation
in these method. Furthermore, we review different proposed trust metrics, as the most important step in this
process.
Comprehensive Social Media Security Analysis & XKeyscore Espionage TechnologyCSCJournals
Social networks can offer many services to the users for sharing activities events and their ideas. Many attacks can happened to the social networking websites due to trust that have been given by the users. Cyber threats are discussed in this paper. We study the types of cyber threats, classify them and give some suggestions to protect social networking websites of variety of attacks. Moreover, we gave some antithreats strategies with future trends.
This document discusses methods for measuring privacy in online social networks. It first introduces the concept of the Privacy Index (PIDX), which quantifies a user's privacy exposure based on their visible attributes. It then describes calculating a user's Privacy Quotient (PQ) using a naive approach, which considers the sensitivity and visibility of shared profile items. Finally, it proposes a new model called Privacy Armor that would measure privacy leaks in unstructured data like posts and messages using an Item Response Theory model to calculate sensitivity, visibility, and privacy quotient. The goal is to warn users about unintended sharing of private information online.
The document summarizes a research paper that proposes a personalized recommendation approach combining social network factors like interpersonal interest similarity and interpersonal rating behavior similarity. It uses probabilistic matrix factorization to predict ratings by considering these social network factors. The approach is evaluated on two large real-world social rating datasets and shows improved performance over approaches that only use social network information.
This document proposes a method for completing user profiles using their online social circles. It introduces the task of user profile completion and discusses how existing approaches do not leverage users' real social circles. The method presented uses non-negative matrix factorization to decompose a circle-profile matrix into a circle-user matrix and user-profile matrix. This allows the model to detect a user's multi-dimensional social characteristics from their various social circles. Experimental results on Facebook, LinkedIn, and Microsoft Academic Search datasets show the approach outperforms state-of-the-art methods for user profile completion.
Identity Resolution across Different Social Networks using Similarity Analysisrahulmonikasharma
Today the Social Networking Sites have become very popular and are used by most of the people. This is because the Social Networking sites are playing different roles in different fields and facilitating the needs of its users from time to time. The most common purpose why people join in to these websites is to get connected with people and sharing information. An individual may be signed in on more than one Social Networking Site so identifying the same individual on different Social Networking sites is a task. To accomplish this task the proposed system uses the Similarity Analysis method on the available information details.
Studying user footprints in different online social networksIIIT Hyderabad
This document describes research on linking a user's accounts across multiple online social networks. It discusses the challenges in linking accounts as usernames and profiles can differ across networks. Existing techniques for linking are reviewed, along with their limitations. The paper then presents a new supervised learning approach to link Twitter and LinkedIn accounts based on similarity metrics for different profile fields. Evaluation shows the approach can accurately match accounts with 98% accuracy and discover new candidate matches for a given user profile.
User service-rating-prediction-by-exploring social users rating BehavioursShakas Technologies
This document proposes a user-service rating prediction approach that explores social users' rating behaviors. It focuses on aspects of users' rating behaviors like when they rate items, what the ratings and items are, and how ratings diffuse among social friends. The approach represents rating schedules and models interpersonal rating diffusion. It fuses personal interest factors, interpersonal interest similarity, rating behavior similarity, and rating diffusion into a matrix factorization framework. The proposed approach aims to address limitations of existing collaborative filtering recommender systems like increased computational costs, lack of privacy, and insecure computations.
This document discusses the shift from the traditional web to the social web, where users engage in more complex online interactions and activities. It introduces Davai's approach to predictive user modeling based on analyzing users' activity streams on social networks to generate models that can predict user demographics, behaviors and interests. Key areas of investment include online marketing, personalized content and context-aware mobile services, with all services being permission-based and providing value to users. Davai analyzes social network communication and uses machine learning on activity stream data to generate predictive user models that classify users into interest and response categories.
The document proposes a methodology for using social network analysis to better understand human behaviors and elicit software requirements for online social networks. It begins with background on online social networks and their failures. It then outlines a novel methodology involving collecting real-world social network data through questionnaires, analyzing the network's structural characteristics like centrality measures, and examining how friendship strength and everyday habits are associated. The case study found unexpected centrality results and demonstrated how social network analysis can provide insights for improved software requirement elicitation.
Determining Strategic Value of Online Social Engagementsinventionjournals
Over the past few decades social networking connections through individuals and open publishing in general have rapidly became a popular tool for maintaining relationships, communicating and expanding businesses. Individuals invest hours in building social capital and their social identify (SID) via online engagements. We present a methodology to quantify the multitude of artifacts that can be derived from online social engagements and develop a framework that measures the value of an individual's online social engagements. ASID value is used to deliver a score for each individual user; a score that will assist you in understanding your return on investment (ROI)and social capital from your online social networking activities. The framework creates a score to support and determine which specific engagements add and increase your personal value chain. This score can provide benefit to users for career, personal, and business opportunities.
APPLYING THE TECHNOLOGY ACCEPTANCE MODEL TO UNDERSTAND SOCIAL NETWORKING ijcsit
This study examines the individuals’ participation intentions and behaviour on Social Networking Sites (SNSs). For this purpose, the Technology Acceptance Model (TAM) is utilized and extended in this study through the addition of “perceived social capital” construct aiming to increase its explanatory power and predictive ability in this context. Data collected from a survey of 1100 participants and distilled to 657 usable sets has been analysed to assess the predictive power of proposed model via structural equation modelling. The model proposed in this study explains 56% of the variance in “Participation Intentions” and 55% of the variance in “Participation Behaviour”. Participation of behavioural intention in the model’
explanatory power was the highest amongst the constructs (able to explain 28% of usage behaviour).While, “Attitude” explain around 11% of SNSs usage behaviour. The study findings also show that “Perceived Social Capital” construct has a notable impact on usage behaviour, this impact came indirectly through its direct effect on “Attitude” and “Perceived Usefulness”. Participation of “Perceived Social Capital” in the models' explanatory power was the third highest amongst the constructs. “Perceived Social Capital”, alone explain around 9% of SNSs usage behaviour.
IRJET- Social Network Mental Disoreders DetectionIRJET Journal
The document presents a study on detecting mental disorders from social network data using machine learning techniques. It aims to detect three types of social network mental disorders: Cyber-Relationship Addiction, Net Compulsion, and Information Overload. The proposed system extracts features from a dataset of 3126 social network users and trains a tensor model to derive latent factors and classify individuals according to their risk of developing an addiction. The system aims to identify mental disorders at an early stage by analyzing patterns in individuals' social media usage data over time.
This document discusses social networking services (SNS) and how they are changing communication and online interactions. SNS allow users to create profiles, connect with others, share content, and communicate both publicly and privately. The UK has high rates of SNS usage. Popular SNS like Facebook, MySpace, and Bebo are profile-focused, while others like Flickr and YouTube center around sharing photos and videos. SNS are accessed both through websites and mobile apps. Young people are early adopters of new SNS features but must also learn to navigate risks. The document provides definitions of SNS and categorizes different types, including those based on profiles, content, groups, virtual environments, mobile access, and microblogging
Similar to IEEE - 2016 Active Research Projects - 1 Crore Projects (20)
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
2. RUAN et al.: PROFILING ONLINE SOCIAL BEHAVIORS FOR COMPROMISED ACCOUNT DETECTION 177
the clickstreams. Moreover, considering that for a spammer,
who carries very different social interests from those of
regular users (e.g., mass spam distribution vs. entertaining
with friends), it is very costly to mimic different individual
user’s social interaction patterns, as it will significantly reduce
spamming efficiency.
In sight of the above intuition and reasoning, we first
conduct a study on online user social behaviors by collecting
and analyzing user clickstreams [6], [15], [23], [26] of a
well known OSN website. Based on our observation of user
interaction with different OSN services, we propose several
new behavioral features that can effectively quantify user
differences in online social activities. For each behavioral fea-
ture, we deduce a behavioral metric by obtaining a statistical
distribution of the value ranges, observed from each user’s
clickstreams. Moreover, we combine the respective behavioral
metrics of each user into a social behavioral profile, which
represents a user’s social behavior patterns.
To validate the effectiveness of social behavioral profile
in detecting account activity anomaly, we apply the social
behavioral profile of each user to differentiate clickstreams of
its respective user from all other users. We conduct multiple
cross-validation experiments, each with varying amount of
input data for building social behavioral profiles. Our
evaluation results show that social behavioral profile can
effectively differentiate individual OSN users with accuracy
up to 98.6%, and the more active a user, the more accurate
the detection.
The remainder of this paper is organized as follows.
Section II surveys related work. Section III introduces the
features we use to characterize a user’s behavior and our
measurement study on those features. Section IV illustrates
the metrics of each feature and how the behavioral profiles
are built; we specify how to differentiate behavioral profiles
and its application to detect compromised accounts. Section V
verifies the feasibility to distinguish users given their behavior
profiles and the detection accuracy to discern different users.
Section VI discusses the advantages and limitation of our
method. Section VII concludes the paper.
II. RELATED WORK
Schneider et al. [15] and Benevenuto et al. [6] measured
OSN users’ behaviors based on network traffic collected from
ISPs. Both works analyze the popularity of OSN services,
session length distributions, and user click sequences among
OSN services, and discover that browsing accounts for a
majority of users’ activities. Benevenuto et al. [6] further
explored user interactions with friends and other users mul-
tiple hops away. While these works primarily emphasize the
overall user OSN service usage, and aim to uncover general
knowledge on how OSNs are used, this paper studies users’
social behavior characteristics for a very different purpose.
We investigate the characterization of individual user’s social
behaviors to detect account usage anomaly. Moreover, we
propose several new user behavioral features and perform
measurement study at a fine granularity. Viswanath et al. [20]
also aim to detect abnormal user behaviors in Facebook, but
they soly focus on “like” behaviors to detect spammers.
While most previous research on malicious account
detection cannot differentiate compromised accounts from
spam accounts, Egele et al. [8] specifically studied the
detection of compromised accounts. By recording a user’s
message posting features, such as timing, topics and correla-
tion with friends, they detected irregular posting behaviors; on
the other hand, all messages in a certain duration are clustered
based on the content, and the clusters in which most mes-
sages are posted by irregular behaviors are classified as from
compromised accounts. While they also leveraged certain user
behavior features to discern abnormality, we use a different
and more complete set of metrics to characterize users’ general
online social behaviors, instead of solely focusing on message
posting behaviors. Additionally, our technique does not rely
on deep inspection and classification of message contents and
avoids the heavy weight processing.
Wang et al. [23] proposed an approach for sybil account
detection by analyzing clickstreams. They differentiated sybil
and common users’ clicks based on inter-arrival time and click
sequence, and found that considering both factors leads to
better detection results. Since sybils are specialized fake identi-
ties owned by attackers, their clickstream patterns significantly
differ from those of normal users. However, for compromised
accounts, their clickstreams can be a mix from normal users
and spammers, As a result, methods in [23] cannot handle
compromised accounts well. In contrast, this paper aims to
uncover users’ social behavior patterns and habits from the
clickstreams, with which we can perform accurate and delicate
detection on behavioral deviation.
Regarding spammer detection, Lee et al. [13] and
Stringhini et al. [17] set up honeypot accounts to harvest
spam and identify common features among spammers, such
as URL ratio in their messages and friends choice; using
those features, both employ classification algorithms to detect
spammers. Yang et al. [28] introduced new features of spam-
mers involving with their connection characteristics to achieve
better accuracy. Thomas et al. [19] analyzed the features of
fraudulent accounts bought from the underground market and
developed a classifier using the features to retrospectively
detect fraudulent accounts. Instead of focusing on malicious
accounts, Xie et al. [25] proposed to vouch normal users based
on the connections and interactions among legitimate users.
As for spam detection, Gao et al. [9] proposed a real-
time spam detection system, which consists of a cluster
recognition system to cluster messages and a spam classifier
using six spam message features. Thomas et al. [18] thrived
to detect spam by identifying malicious URLs in message
content. In [10] and [12], the authors conducted offline
analysis to characterize social spam in Facebook and Twitter,
respectively. They found that a significant portion of spam
was from compromised accounts, instead of spam accounts.
Meanwhile, Yang et al. [27] investigated connections among
identified spammers and other malicious account detection
methods [7], [16], [21] exploit the differences on static profile
or connectivity information between normal and malicious
accounts.
Users’ social behavior analysis has also been employed
to serve other purposes. Wilson et al. [24] analyzed user
3. 178 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 11, NO. 1, JANUARY 2016
interactions with friends from the trace of Facebook profiles
to improve performance for sybil detection while reducing
its complexity. In [5] and [14], the authors correlated users’
personalities with their OSN service usages.
III. USER SOCIAL BEHAVIORS STUDY
In this section, we first propose several social behavior
features on OSNs, and describe in detail how they can
reflect user social interaction differences. Then, we present
a measurement study on user behavior diversity by analyzing
real user clickstreams of a well known OSN, Facebook, with
respect to our proposed features.
A. Social Behavior Features
We categorize user social behaviors on an OSN into
two classes, extroversive behaviors and introversive behaviors.
Extroversive behaviors, such as uploading photos and sending
messages, result in visible imprints to one or more peer
users; introversive behaviors, such as browsing other users’
profiles and searching in message inbox, however, do not
produce observable effects to other users. While most previous
research only focus on the extroversive behaviors, such as
public posting [8], we study both classes of behaviors for
a more complete understanding and characterization of user
social behaviors.
1) Extroversive Behavior Features: Extroversive Behaviors
directly reflect how a user interacts with its friends online,
and thus they are important for characterizing a user’s social
behaviors. We specify extroversive behaviors on the following
four major aspects.
◦ First Activity:
The first extroversive activity a user engages in after logging
in an OSN session can be habitual. Some users often start
from commenting on friends’ new updates; while some
others are more inclined to update their own status first.
The first activity feature aims to capture a user’s habitual
action at the beginning of each OSN session.
◦ Activity Preference:
How often a user engages in each type of extroversive
activities relates to their personalities [5]. Some users like
to post photos, while some others spend more time respond-
ing to friends’ posts; some mostly chat with friends via
private messages, while some others always communicate
by posting on each other’s public message boards. Typical
OSNs provide a great variety of social activities to satisfy
their users’ communication needs, for example, commenting,
updating status, posting notes, sending messages, sharing
posts, inviting others to an event, etc. As a result, this
feature can provide a detailed portrayal of a user’s social
communication preferences.
◦ Activity Sequence:
The relative order a user completes multiple extroversive
activities. While users have their preferences on different
social activities, they may also have habitual patterns when
switch from one activity to another. For instance, after
commenting on friends’ updates, some users often update
their own status, while some other users prefer to send
messages to or chat with friends instead. Therefore, the
action sequence feature reflects a different social behavioral
pattern from the activity preference.
◦ Action Latency:
The speed of actions when a user engages in certain
extroversive activities reflects the user’s social interaction
style. Many activities on OSNs require multiple steps to
complete. For example, posting photos involves loading
the upload page, selecting one or more photos, uploading,
editing (e.g., clipping, decorating, tagging, etc.), previewing
and confirmation. The time a user takes to complete each
action of a given activity is heavily influenced by the user’s
social characteristics (e.g., serious vs. casual) and familiarity
with the respective activity; but it doesn’t directly reflect
how fast a user acts due to different content complexity.
The action latency feature is proposed to provide more fine-
grained and accurate metric.
2) Introversive Behavior Features: Although invisible to
peer users, introversive behaviors make up the majority of
a user’s OSN activity; as studied in previous work [6], [15]
the dominant (i.e., over 90%) user behavior on an OSN is
browsing. Through introversive activities users gather and
consume social information, which helps them to form ideas
and opinions, and eventually, establish social connections
and initiate future social communications. Hence, introversive
behavior patterns make up an essential part of a user’s online
social behavioral characteristics. We propose the following
four features to portray a user’s introversive behavior.
◦ Browsing Preference:
The frequence a user visits various OSN page types depicts
its social information preferences. Typical OSNs classify
social information into different page types. For instance,
profile pages contain personal information of the account
owners, i.e., names, photos, interests etc.; the homepage
compose of the account owner’s friends’ latest updates while
a group page consists posts or photos shared by group
members. Users’ preferences on various types of social
information naturally differ by their own interests, and the
browsing preference feature intends to reflect this difference
by observing users’ subjective behaviors.
◦ Visit Duration:
The time a user spends on visiting each webpage depicts
another aspect of its social information consumption. Intu-
itively, users tend to spend less time on information that
are “good-to-know”, while allocate more time on consuming
information that are “important”, and their judgments are
made based on their own personal interests. For example,
some users prefer to stay on their own homepage reading
friends’ comments and updates, while some others tend to
spend more time reading others’ profile pages. The visit
duration feature aims at capturing the social information
consumption patterns for different users.
◦ Request Latency:
During a single visit to a webpage, a user may request mul-
tiple pieces of information. For example, browsing through
a photo album requires loading each photo inside the album;
4. RUAN et al.: PROFILING ONLINE SOCIAL BEHAVIORS FOR COMPROMISED ACCOUNT DETECTION 179
Fig. 1. Facebook Data Set Overview Statistics. (a) Cumulative Session Lengths. (b) Single Session Length Distribution.
reading comments from friends may also require “flipping”
through many “pages” because only a limited amount of
entries can be displayed at a time. Similar to the action
latency feature for extroversive activities, the request latency
feature provides fine-grained characterization of users’ social
information consumption patterns.
◦ Browsing Sequence:
The order a user switches between different webpages
reflects a user’s navigation patterns amongst different types
of social information. OSNs usually provide easy navigation
for users to move around various pages; a user on a friend
profile page can directly navigate to another’s profile page,
or go back to the homepage and then go to another friend’s
profile page. How each user navigates during browsing
can be habitual, and this feature intends to capture this
characteristics.
B. Facebook Measurement Study
We conduct a measurement study of Facebook users to
understand their online social behaviors. In order to observe
both extroversive and introversive behaviors from the partic-
ipating users, we develop a browser extension to record user
activities on Facebook in the form of clickstreams. In the
following, we first present our data collection methodology
and techniques, and an overview of the collected data set.
Then, we detail the measurement results of user behavioral
features.
1) Data Collection: We have recruited a total of
50 Facebook users for our measurement study—22 are grad-
uate students at universities and the rest are recruited via
Amazon Mechanical Turk or Odesk, both of which are popular
online crowdsourcing marketplaces. For each user, we collect
approximately three weeks of their Facebook activities.
To ensure that the recruited users are actually normal Facebook
users, we use their first week as “trial” periods, during which
we conduct manual review on the collected activity data.
The clickstreams in our dataset are organized in units of
“sessions”. We denote the start of a session when a user
starts to visit Facebook in any window or tab of a browser;
the end of a session is denoted when the user closes all
windows or tabs that visit Facebook, or navigates away from
Facebook in all windows or tabs of the browser. Clickstreams
from concurrently opened tabs/windows are grouped into a
single session, but are recorded individually (i.e., events from
one window/tab are not merged with those from another
window/tab). In total, we have collected 2678 sessions.
We further process each clickstream before performing
detailed measurement analysis. We detect and remove click-
streams in the “idle” periods—significantly long time intervals
in which no user activity is observed, by analyzing the request
timestamp and URLs. For example, users may go away from
their computers while leaving their browsers running. With
idle periods removed, we plot the “effective” cumulative
clickstream lengths for each participating user in Figure 1(a).
We observe that the clickstream lengths follow exponential
distribution. During a three-week period, the least active user
only accumulates half an hour of activities, while the most
active user spends more than 98 hours on Facebook. We also
plot the Cumulative Distribution Function (CDF) of single
session lengths across all users in Figure 1(b). It is evident
that the distribution of single session length is heavy-tailed.
While over 66% of sessions are within 300 seconds, more
than 11% of sessions are over 2000 seconds long.
2) Feature Measurements: We first conduct a systematic
study of services and webpages on Facebook. Based on request
URL, we categorize 29 different types of extroversive activities
that a user can conduct to interact with peer users; we also
classify 9 types of Facebook webpages containing different
kinds of social information, which users can browse privately
(i.e., the introversive activities). With the mapping between the
clickstream information and the user behaviors, we analyze
each user’s clickstreams to extract the corresponding behavior
patterns. We present the combined measurement results of
each behavior feature for all users to show the value space, and
finally we use an example to illustrate user behavior diversities.
Figure 2(a) shows the distribution of first activity in users’
OSN sessions. From this figure, we can observe that there
are four most favorite activities, “like”, “send message”, “use
app”, and “comment”, which account for about 80% of all
29 activities. The rest 25 types of activities are much less
frequently engaged in by users, and account for about 20%
of all activities. While the “send message”, “use app”, and
“comment” activities have comparable proportions, the “like”
activity has an obviously higher preference than the other
three. The extraordinarily tall error bars indicate that users
have significant diversities over all types of activities.
Figure 2(b) shows the distribution of extroversive activities
users involve in, i.e., the activity preference. The primary
5. 180 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 11, NO. 1, JANUARY 2016
Fig. 2. Combined Distributions of Extroversive Features. (a) First Activity. (b) Activity Preference. (c) Action Latency.
Fig. 3. Combined Distributions of Introversive Features. (a) Browsing Preference. (b) Visit Duration. (c) Request Latency.
features of this figure are similar to those of Figure 2(a): the
same four activities dominate the distribution, and the user
diversities on all activities are also very significant. However,
there are some minor but observable differences. First, the
relative orders of the four most favorite activities are different
from Figure 2(a); in addition, the relative proportions of the
four activities are more comparable. Second, the “add photo”
and “share” emerge as “significant” activities (having a > 2%
share of the total), while the “Like a page” and “post status”
activities become “insignificant”. These differences indicate
that user behaviors do vary significantly across different behav-
ioral contexts.
We discern the user action latency by first grouping
clickstreams belonging to each user activity type, and then
measuring the interarrival time of consecutive HTTP requests
within each group of clickstreams. The distribution of action
latencies is presented in Figure 2(c), from which we can
observe that users generally interact with services at a fast
pace, over 90% of actions have less than 7 seconds delay
in-between. Action latencies in the 0 to 9 second range
are diverse between individual users, resulting in tall error
bars.
The user browsing preference distribution is shown
in Figure 3(a). We can see that visits to the “homepage”
and “profile” account for 86% of all user browsing actions.
Similar to Figures 2(a) and 2(b), large user diversities man-
ifest on all types of webpages.1 Figure 3(b) presents the
distribution of webpage visit duration. With a heavy-tailed
1We did not observe enough “notes” and “notification” page visits to derive
meaningful statistics, so their data are considered as unknown.
distribution, over 90% of visits last less than 600 seconds.
Users tend to have highly divergent behaviors for visit duration
in the 0 to 3 minute range. We study the request latency using
the same technique for measuring action latency, and observe
similar results. As shown in Figure 3(c), with 90% of inter-
request delays less than 10 seconds, the latencies for request
sending during webpage browsing are generally slightly larger
than those for engaging in extroversive activities. User diver-
gence is the most obvious in the 0 to 9 second range.
We further study the action latency and request latency
using the burstiness parameter [11]. The burstiness parameter
has a value range of [−1, 1], with 1 denoting complete
randomness, and lower values signifying more regular behav-
iors. We find that, for action latency, the average value of
burstiness parameters is −0.12, and 82% of the users’ bursti-
ness parameters are negative, indicating that individual users
tend to have regular action patterns; for request latency, the
average burstiness parameter value is 0.035, which indicates
slightly more randomness than action latency. In addition,
74% of users have lower burstiness parameter values for profile
browsing than those for homepage browsing, indicating user
browsing speed tends to be more random on homepage than
on profile.
The activity sequence and browsing sequence consist of the
distributions of usage for all pair-wise combinations of extro-
versive activities and webpages, respectively. Due to the large
number of activities and webpages, the possible value spaces
for these two features are very large. Normal user activities
tend to explore only a small portion of these feature value
spaces. We observe that, on average, each user’s extroversive
activities only include 9.3 out of 841 (1.1%) possible
6. RUAN et al.: PROFILING ONLINE SOCIAL BEHAVIORS FOR COMPROMISED ACCOUNT DETECTION 181
TABLE I
FEATURE VALUE COMPARISON
activity combinations, and each user’s webpage visits only
include 7.3 out of 81 (9%) possible webpage combinations.
Our measurement study shows that we can discern user
online social behavior characteristics by analyzing their click-
streams. The results confirm that given a large number of
social activities, individual OSN users tend to have diverse
behavior patterns. We illustrate the diversity with an example.
We randomly pick two users from our data set and present
the most significant factors of each user’s behavioral features,
side-by-side, in Table I. From this table, we can observe that
nearly every behavioral feature of these two users differs,
implying that it is possible to tell behaviors of two users apart
by comparing those feature values.
IV. PROFILING SOCIAL BEHAVIORS
In this section, we first detail the formation of a user social
behavioral profile using our proposed behavioral features.
Based on our Facebook measurement study, we quantify
Facebook user behavior patterns into a set of eight fine-grained
metrics that correspond to the eight social behavioral features.
The social behavior profile of an individual user can thus be
built by combining the respective social behavioral metrics.
Then, we describe the application of social behavior profiles
in differentiating users and detecting compromised accounts.
A. Facebook User Behavioral Profile
In order to quantify user social behavior patterns on a spe-
cific OSN, we must first convert the social behavioral features
into concrete metrics. We apply our knowledge gained in
the Facebook measurement study, and devise a quantification
scheme for each behavioral feature as follows.
◦ The first activity metric is defined as a 29-element vector,
with each element corresponds to an extroversive activity
on Facebook. The value of each element is the empirical
probability a user engages in the associated activity as the
first extroversive activity in a browser session.
◦ The activity preference metric is also a 29-element vector,
similar to the first activity metric. The value of each element
is the empirical probability a user engages in the associated
activity throughout a browser session.
◦ The activity sequence metric is defined as a 29×29-element
vector. If we conceptually arrange the vector as a
29-by-29 matrix, each cell of the matrix represents a transi-
tion between two Facebook extroversive activities a1 → a2,
whose indices are reflected by the row or column number of
the cell. The value of each cell is the probability of a user
to transit to activity a2 from activity a1.
◦ The action latency metric is defined as an 11-element vector,
and it records the empirical probability distribution of delays
between consecutive HTTP requests while a user performs
extroversive activities. We define the initial duration as zero,
the first ten elements as one-second-wide bins, and element
eleven as an infinite-time-width bin.
◦ The browsing preference metric is defined as a 9-element
vector. Each element corresponds to a type of webpage on
the Facebook website. The value of each element is the
empirical probability a user visits the associated webpage
throughout a browser session.
◦ The visit duration metric is defined as a 3 × 15-element
vector, and each group of 15 elements records the empir-
ical probability distribution of the duration a user visits
homepages, profile pages or application page, respectively.2
For each 15-element vector, we define the initial duration
as zero, the first ten elements as 30-second-wide bins, the
following four elements as 60-second-wide bins, and the
fifteenth element as an infinite-time-width bin.
◦ The request latency metric is also a threefold 11-element
vector, and each group of 11 elements records the empirical
probability distribution of delays between consecutive HTTP
requests during a user’s visits to homepages or profile pages
or application pages, respectively. Similar to the action speed
metric, the initial duration is zero, element one through ten
are one-second-wide bins, and element eleven is an infinite-
time-width bin.
◦ The browsing sequence metric is defined as a
9 × 9-element vector. Similar to the activity sequence
metric, we conceptually arrange the vector as a 9-by-9
matrix, and each cell of the matrix represents a transition
between browsing two types of Facebook webpages
p1 → p2, whose indices are reflected by the row or column
number of the cell. The value of each cell is the probability
of the user to switch to type p2 after browsing page type p1.
With concrete behavioral metrics in hand, we build a
Facebook user’s social behavioral profile by first combining
their social behavior metrics into an 8-vector tuple, then
normalizing each vector so that the sum of all elements in
a vector equals to one. In particular, the visit duration and
request latency vectors are multiplied by a factor of 1/3; the
activity sequence vectors and the browsing sequence vectors
are multiplied by 1/29 and 1/9, respectively, while all other
metrics are unchanged. Table II lists the sample of a Facebook
user social behavioral profile.
B. Differentiating Users
The social behavioral profile depicts various aspects of a
user’s online social behavior patterns, and it enables us to
2We do not consider other 6 types of webpages because user visits on these
pages only account for less than 8.8% of all browsing activities.
7. 182 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 11, NO. 1, JANUARY 2016
TABLE II
A BEHAVIORAL PROFILE SAMPLE
quantitatively describe the differences in distinct user social
behaviors. In the following, we first describe how to com-
pare social behavioral profiles by calculating their difference.
Then, we discuss the application of social behavioral profile
comparison to distinguishing different users and detecting
compromised accounts.
1) Comparing Behavior Profiles: Given any two social
behavioral profiles, P and Q, we quantify their difference in
two steps.
In the first step, we compare each of the eight vectors in P
against the respective vector in Q. Particularly, we measure
the Euclidean distance to quantify the difference between
the two vectors. Given two vectors A = (a1, a2, . . . an) and
B = (b1, b2, . . . bn), the Euclidean distance between them is
calculated by
E(A, B) =
n
i=1
(ai − bi)2.
Comparing all eight vectors yield an eight-element Euclidean
distance vector (E1, E2, . . . , E8). Each element in this vector
has a range of [0,
√
2], because the sum of each vector’s
elements is one.
In the second step, we take the Euclidean norm of the
Euclidean distance vector,
D(P, Q) =
8
j=1
(E j )2.
The resulting value is the difference of the two behavioral
profiles, and has a range of [0, 4]—the more significant the
two profiles differ, the larger the value is.
2) Applying Profile Comparison: To apply the profile com-
parison technique for differentiating users, we must further
introduce another concept, self variance, in addition to the
profile difference.
With two or more distinct pieces of behavioral data
(i.e., clickstreams) collected from the same user, the social
behavioral profiles built from each piece of behavioral data are
not identical. The reasons for the differences are twofold. First,
human behaviors are intrinsically non-deterministic, therefore
a small amount of variation is expected even for the same
activity performed by the same user. Second, because the
social behavioral profile is built on top of statistical obser-
vations, errors always exist for a finite amount of samples.
A user’s average behavior variance is presented. Given a
collection of social behavioral profiles {P1, P2, . . . , Pn} for
a user U, we define the self variance of U as the mean
differences between each pair of profiles:
VU =
n
j=1
n
k=1,k= j D(Pj , Pk)
n(n − 1)
.
The corresponding standard deviation of those differences is
denoted as stdDev(VU ). Thus, with a probability of 97%, U’s
behavior variance falls into [0, VU +2∗stdDev(VU )] (assuming
a user’s behavior profiles comply to normal distribution).
3) Detecting Compromised Accounts: Together with the
self variance, we can apply profile comparison to distinguish
different users and detect compromised accounts. Given a user
U’s behavioral profile PU , self variance VU , stdDev(VU ), and
an unknown social behavioral profile Q, we can decide that the
behavioral profile Q is not user U’s if the difference D(PU , Q)
is larger than VU + n ∗ stdDev(VU ), in which n is adjustable.
A large n would result in a large false negative rate, while a
small n would lead to a large false positive rate.
After building a user’s behavior profile and variance during
a training phase, we can decide whether the user’s account
is compromised. While the method illustrated before can
be employed to fulfill the task, we adjust the method by
personalizing the computation of difference to each user’s
behavior profile.
During the training phase, we first examine the authentic
user U’s consistency on each behavior feature. Given a set
of U’s clickstreams, the corresponding behavior profiles can
be built as Section IV-A depicted. Then we calculate the
average Euclidean distance on each feature vector in U’s
behavior profiles. The eight features are sorted according to the
average distances in an ascending order; then each feature is
assigned a weight that is inversely proportional to its rank. The
weight on each feature is denoted as w1, w2, . . . , or w8. Then
DU (PU , Q) = 8
j=1 wj (E j )2 is employed to compute an
unknown behavior profile Q’s difference to PU .
Giving a weight on each feature is to portray a user’s
degree of consistency on different behavior features, which
is also difficult to feign. User consistency on behavior fea-
tures differs from one to one. The personalized weight on
each feature in the training phase enlarges the distance in
user differentiation. Heavy-weighted behavior features that
a user behaves more consistently on play more important
roles in detecting impostors than light-weighted features. If an
unknown behavior profile belongs to U, it is likely that its
distance on heavy-weighted features are smaller than that on
light-weighted features. For an impostor’s profile that does
not hold this pattern, it is highly likely that the distance
to U on heavy-weighted features is also large, which results
in comparatively larger difference.
To sum up, the detection of account compromisation can
be conducted as follows. During the training phase, given
a collection of clickstreams from the account’s authentic
8. RUAN et al.: PROFILING ONLINE SOCIAL BEHAVIORS FOR COMPROMISED ACCOUNT DETECTION 183
user U, U’s behavior profile PU , weights on the eight fea-
tures w1, w2, . . . , w8 are calculated first as previously stated;
then U’s self variance and the standard deviation of variance
are calculated using the weighted difference formula DU ,
denoted as VU and stdDev(VU ), respectively. U’s behavior
profile PU is built from the union of the clickstreams. For each
incoming clickstream of the account, a behavior profile Q is
built from it; then the difference from Q to PU is calculated
as DU (PU , Q). If DU (PU , Q) ≥ VU + n ∗ stdDev(VU ), then
it is classified as from a non-authentic user, and thus, it
is likely that the account is compromised. To guarantee a
very low false positive rate (less than 3%), n is assigned
to be 2.
As it is possible that a user’s behavior patterns change over
time, the behavior profile needs to be updated periodically
to accurately portray its patterns. While some online habits
remain, a user’s behavior may evolve over time. To capture
the change, the training phase can be repeated using a user’s
latest clickstream to update a user’s behavior profile including
feature weights.
In addition, when there are introduction of new services,
new behavior features may need to be extracted. At the same
time, multiple existing behavior features may also experience
significant changes, which could be large enough to produce
false alarms. This increased false alarm rate cannot be limited
by weighinh potential harmfulness. The training phase also
need to be repeated in this scenario. New training data collec-
tion is required, and it may take some time for the detector to
work accurately again.
4) Incomplete Behavior Profiles: Dependent upon user
activeness in OSNs, the completeness of a user’s behavior
profile varies. The incomplete behavior profiles should be spe-
cially processed while calculating the difference, considering
the lack of sample activities from which metric vectors are
built.
When some feature vectors are not available, they are not
considered while calculating the difference; in this scenario the
final difference will be normalized. For instance, if a user’s
extroversive activity metric vectors are not available due to
the reason that it does not conduct extroversive activities,
its difference to another behavior profile only counts into
the distances on the four introversive activity vectors; for
normalization, the derived distance is multiplied by a factor
of 4/
√
2 × 4 to serve as the final difference.
Furthermore, when there are rare sample activities to build
a metric vector, it is taken as N/A. For example, if there
are only 5 extroversive activities in a clickstream, the activity
preference vector built from them can hardly be representative
of the user’s behavior pattern. Hence, a threshold of the
minimum number of sample activities should be assigned to
guarantee the quality of metric vectors. Those vectors built
from a lower-than-threshold number of sample activities are
taken as N/A.
The varied thresholds of sample activity are assigned to
different feature vectors. For browsing preference vector, it is
possible that 15 page browsing activities are able to derive a
comparatively representative vector; but for browsing sequence
metric, 15 browsing transitions can hardly demonstrate
Fig. 4. Behavior Profile Difference & Variance.
illustrative transition probabilities. Hence, when a sample
threshold is assigned, it is applicable to all features except for
browsing sequence and activity sequence, whose thresholds
are two times of the assigned threshold.
V. EVALUATION
We first verify that behavioral profile can accurately portray
a user’s behavior pattern. Next, we validate the feasibility of
employing behavioral profiles to distinguish different users,
which can be used to detect compromised accounts.
A. Difference vs. Variance
We demonstrate that compared to a user’s behavior variance,
its behavioral profile difference from others is more significant.
That is, a user’s behavioral profile can accurately characterize
its behavior pattern. We compute each sample user’s behavior
variance and behavioral profile differences between its own
and other users’. For each sample user, we equally partition
its clickstream into four complementary parts by session, and
four behavioral profiles are built accordingly; its weights on
each feature are calculated and used for difference calculation.
A 4-fold cross-validation is conducted to calculate its average
behavior variance and the average difference from the others’
behavioral profiles.
Figure 4 shows each user’s behavior variance and the
average behavioral profile difference to others’. Note that only
those users who have sufficient social activities, referred as
“valid" users, are included in the figure. In particular, the
behavioral profile of each valid user must have more than
or equal to 4 non-empty feature vectors, to ensure that the
behavioral profile is complete enough to represent the user’s
behavior patterns. Each feature should be derived from more
than or equal to 10 social activities. For those users whose
social profiles do not meet the requirement above are excluded.
As the figure shows, each user’s self variance is obviously
lower than its average difference from other users. This
coincides with our intuition that a user’s behavior variance
is usually within a certain range; comparatively complete
behavioral profiles can portray users’ behavior patterns. More
importantly, it is possible to take advantage of the difference
between behavioral profiles to discern a user.
In addition, we compare the results between the student
users and the online-recruited users. The difference between
average behavior variance of students and that of online-
recruited users is only 6%. Moreover, using the difference
9. 184 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 11, NO. 1, JANUARY 2016
Fig. 5. Impact of Training Data Size. (a) Training Data Size vs. Accuracy. (b) Valid Users w. Training Data Size.
between self variance and average difference from other users
as a metric, we observe that there is no distinction between
the student users and the online-recruited users. Thus, neither
of the two group of sample users are biased in evaluation.
B. Detection Accuracy
Here we further evaluate the accuracy of using social
behavioral profiles to differentiate online users. We conduct
three sets of experiments by varying training data size, feature
quality, and profile completeness, respectively, to evaluate their
impacts upon the detection accuracy. Both false positive and
false negative are considered as inaccurate detection.
For each sample user U, VU and stdDev(U) are calculated
from its clickstream, and other users are taken as impostors.
For each impostor, we calculate its behavioral profile differ-
ence to U’s using U’s weights on each feature. If the difference
is larger than VU + 2 ∗ stdDev(U), the decision is taken as
correct; otherwise, it is taken as an error. The average error
rate is calculated in each scenario. Setting the threshold to be
VU + 2 ∗ stdDev(U) guarantees that U’s behavioral profiles
can be discerned with the probability of more than 97%.
1) Input Size vs. Accuracy: Intuitively, the more training
data are given to build a user’s behavioral profile, the better the
profile reflects its behavior pattern; hence the profile difference
demonstrates the dissimilarity between two user behaviors
more accurately.
We build each user’s behavioral profile using the clickstream
from 1/6, . . ., and 1/2 of its total sessions, respectively,
and use cross-validation to compute and compare behavioral
profile differences. Take the 1/6 of sessions as an example,
each user’s clickstream is partitioned into 6 parts while the first
part includes the clickstream from the 1st, 7th, 13th, . . ., ses-
sions; the second part includes the clickstream from the 2nd,
8th, 14th, . . ., sessions etc. Six behavioral profiles are built
accordingly and each profile is used for difference calculation.
For user A, when we use each part of its clickstream to build
its behavioral profile, the behavioral profile difference from
another user B to user A is calculated six times, each of which
considers A’s behavioral profile and one of B’s behavioral
profiles, which is built from one out of its 6 clickstreams.
Cross-validation is used to make sure that each part of data
are used for both training and validation, and the result is not
derived from biased data. Furthermore, we only consider users
whose behavioral profiles consist of more than or equal to 4
non-empty feature vectors, each of which should be built from
more than or equal to 15 sample activities. The thresholds are
set to guarantee the vector quality as well as the complete-
ness of the behavioral profiles. To give more straightforward
impression over the size of clickstream we calculate its average
active hours in each partition.
Figure 5(a) shows the dynamics of the error rate with the
change of the clickstream length, while Figure 5(b) shows the
number of valid users with different data partitions. Overall,
the longer is the clickstream, the more accurate is the detec-
tion. When the clickstream is up to 8.9 hours, the error rate
can reach 9.5% while longer clickstreams derive better result.
When it is more than 9.3 hours, the error rate is as small
as 0.3%. Longer clickstream provides more empirical behavior
data of a user, which enables us to build more accurate and
complete behavioral profiles, and hence the distance on each
behavioral feature can be measured more accurately. Thus,
the detection is more accurate. On the other hand, with the
finer partition of the clickstream, the fewer activities in each
partition, leading to the smaller number of non-empty vectors
and the smaller number of valid users.
2) Feature Quality vs. Accuracy: We adjust the threshold of
the number of sample activities to explore whether the feature
vector quality affects the detection accuracy.
When building a user’s behavioral profile, the number
of sample activities that derive a feature vector determines
whether the feature vector represents a user’s behavior
accurately. By assigning a threshold to the number of sample
activities, we can take control over the quality of feature
vectors. We designate those vectors derived from insufficient
activities to be N/A. Intuitively, higher sample activity
threshold results in feature vectors with higher quality, which
reduces the noise of behavior variance introduced by rare
sample activities. Thus, the difference between users can be
discerned more accurately.
The detection accuracy is evaluated when we assign the
sample threshold to be 10, 15, 20, 25, and 30, respectively.
We use the 1/4 partition of clickstreams to build user behav-
ioral profiles and those users with less than 4 feature vectors
are excluded. Same as before, 4-fold cross-validation is used
to derive the average detection accuracy. The error rate with
different sample threshold is depicted in Figure 6(a). It verifies
our intuition that larger sample generates feature vectors
with higher quality, which helps to differentiate behavioral
10. RUAN et al.: PROFILING ONLINE SOCIAL BEHAVIORS FOR COMPROMISED ACCOUNT DETECTION 185
profiles. The side-effect to set a high sample threshold is that
with limited clickstream, fewer feature vectors can be built,
resulting in fewer valid users, as Figure 6(b) shows.
3) Profile Completeness vs. Accuracy: Due to the lack of
certain activities, some behavioral feature vectors can be N/A.
For instance, when one never conducts extroversive activities
in its clickstream, at least four of its feature vectors are N/A,
which makes its profile incomplete. By adjusting the least
number of non-empty features vectors, the completeness of
selected behavioral profiles can be guaranteed.
Here we adjust the threshold of the vector number to
be 3, 4, 5, 6, 7, respectively, to examine the impact of behav-
ioral profile completeness upon the detection accuracy. Same
as above, the behavioral profile is built from the clickstream in
1/4 of sessions and the sample threshold is 15. A 4-fold cross-
validation is conducted to compute the average accuracy. The
detection error rate with the change of the vector threshold is
shown in Figure 7(a), while Figure 7(b) shows the number of
valid users in each case. Evidently, more complete behavioral
profiles result in higher detection accuracy, which validates
the effectiveness of the behavioral features for user behavior
characterization. With 7 feature vectors available, the detection
accuracy reaches 98.6%.
Overall, active users can be distinguished more accurately
by their behavioral profiles compared to inactive users. The
more types of activities a user conducts, the more complete its
behavior profile can be. And the more activities a user conduct,
the more sample activities can be obtained within a certain
duration, leading to more accurate behavioral profile. On the
other hand, as compromised accounts are usually manipulated
to become active to spread spam, there will be a sudden change
of behavior when an inactive user account is compromised.
Thus, we can still detect the compromisation of an inactive
user account, even without its accurate and complete behavior
profile.
VI. DISCUSSION
In this section, we first describe how social behavior pro-
filing can be applied to detect abnormal behavoirs of compro-
mised accounts. Then we discuss adaptions of our technique
to special OSN accounts (such as multi-user shared accounts
and public social media accounts). We also discuss how to
handle those accounts created by normal users but rarely used.
Finally, we discuss the applicability of our approach to non-
user-interactive channels and its limitation.
A. Detecting Compromised Behaviors
By building a behavioral profile along with the self variance
for an account, we can decide whether incoming clickstreams
from the account is from the authentic user or a different user.
If an incoming clickstream is from the authentic user, to some
extent it should comply with the behavior pattern represented
by the account’s behavior profile.
When an account is compromised and its behavior is well-
optimized to post spam, the detection accuracy should be
higher than that of differentiating another normal user. With
a clear objective, to broadcast spams, spammers usually act
goal-orientedly [23]. Compromised accounts can be well-
programmed to focus on posting spams. Thus, their behaviors
evidently deviate from common users’ behaviors that are
spontaneous and out of interests. As a result, the higher chance
is that a clickstream consists of aggressively posting activities
largely diverging from the account’s behavior profile built from
unprompted activities.
Compared to an account’s evident social characteristics,
such as language and interests, its social behavior is harder
to feign. Even though spammers do not adopt aggressive
strategies to post spams and manipulate compromised accounts
to browse randomly or slow down the post speed to look
normal, it is hard for them to obtain the authentic user’s
unconscious social behavior pattern, not to mention to feign.
Note that our method is not limited to detect compromised
accounts that are manipulated to spread spams in a specific
way, such as post on the message board. Since most existing
spam detection methods are tied to detect spam messages [8],
[9], [17], if spam is embedded in other media, such as
photos or shared files, those schemes are not applicable for
detection anymore. However, using social behavior profiles to
detect compromised accounts is independent from the forms
of spams.
Moreover, our method can be adopted in combination
with existing schemes to battle against account hijacking.
In comparison with existing detection approaches, either URL
or message content analysis based, our social behavior based
method discerns compromised accounts from very different
perspectives. Our method can serve as a complementary mea-
sure to existing detection solutions.
B. Handling Special Accounts
Some special accounts should opt out this compromisation
detection scheme voluntarily in advance. Although an OSN
account is normally owned by an individual user, it happens
that an account is shared by multiple users. In this case, the
account’s behavior variance can be much larger than that of
an account managed by a single user. On one hand, such a
shared account could be wrongly classified as a compromised
account. (i.e., producing a false positive). On the other hand,
if its behavior variance was employed as the standard to
detect account compromisation, the false negative rate would
be larger than using that of a single-user account.
Some public page accounts should opt out too, such as
the public pages registered by business companies to promote
their products. Their behaviors are also different from those of
normal users since they are also goal-oriented. It is possible
that those accounts are only manipulated to post ads or news.
Thus, their behavior patterns are more similar to spammers
than normal users.
C. Rarely Used Accounts
There exist some normal inactive OSN accounts, i.e., those
accounts are created by normal users but are rarely used after
the creation. Since these accounts are inactive most of time,
it is hard to obtain their complete social behavioral profiles.
However, on one hand, we can still build a profile for such
11. 186 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 11, NO. 1, JANUARY 2016
Fig. 6. Impact of Feature Quality. (a) Feature Quality vs. Accuracy. (b) Valid Users w. Feature Quality.
Fig. 7. Impact of Profile Completeness. (a) Profile Completeness vs. Accuracy. (b) Valid Users w. Profile Completeness.
an inactive account as long as its owner logs in at least once.
On the other hand, it would be more straightforward to detect
the compromisation of such an inactive account because we
can simply employ the existing solutions, such as checking its
posting message behavior and message content, for anomaly
detection.
D. Applicability and Limitation
Our method can be easily adopted to most of existing
popular OSNs besides Facebook such as Google+, Twitter,
and Instagram. The users of those sites are enabled to conduct
various extroversive and introversive behaviors. As long as
the behaviors are categorized, each user behavior profile can
be built based on the eight behavior features we proposed in
Section III-A. Therefore, our approach can be readily applied
for detecting compromised accounts in those OSN sites.
Our method is applicable to accounts whose social behavior
patterns can be profiled, i.e., users who access OSN services
through the Web. For normal users who directly visit the
OSN webpage, their behavioral profile can be easily built via
clickstream. On the other hand, it is hard to trace the behavior
patterns of users who access an OSN solely via APIs, thus,
our method may not be applicable for those rare cases.
However, if a compromised account uses APIs to post spams
aggressively with zero social activities, we can easily detect
such a compromised account given its authentic user accesses
the account via the Web.
Additionally, our method assumes that cyber criminals
cannot easily obtain target users’ online social behavior pat-
terns. However, if more arduous hackers compromise the
physical machines that users own, they are able to learn their
social behavior patterns and mimic the authentic users’ social
behaviors to avoid our detection. However, this requires more
resourceful and determined attackers and costs much more
resources and time, especially in large scale campaign.
VII. CONCLUSION
In this paper, we propose to build a social behavior profile
for individual OSN users to characterize their behavioral
patterns. Our approach takes into account both extrover-
sive and introversive behaviors. Based on the characterized
social behavioral profiles, we are able to distinguish a users
from others, which can be easily employed for compromised
account detection. Specifically, we introduce eight behavioral
features to portray a user’s social behaviors, which include
both its extroversive posting and introversive browsing activ-
ities. A user’s statistical distributions of those feature values
comprise its behavioral profile. While users’ behavior profiles
diverge, individual user’s activities are highly likely to conform
to its behavioral profile. This fact is thus employed to detect
a compromised account, since impostors’ social behaviors can
hardly conform to the authentic user’s behavioral profile. Our
evaluation on sample Facebook users indicates that we can
achieve high detection accuracy when behavioral profiles are
built in a complete and accurate fashion.
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Xin Ruan received the B.S. and M.S. degrees
in computer science from Xidian University,
in 2007 and 2009, respectively. She is currently
pursuing the Ph.D. degree with the Department of
Computer Science, College of William and Mary,
Williamsburg, VA. Her research interests include
privacy issues in online social networks, data mining,
and knowledge discovery.
Zhenyu Wu received the Ph.D. degree in computer
science from the College of William and Mary,
in 2012. He is currently a Research Staff Member
with NEC Laboratories America Inc., Princeton, NJ.
His research focuses on enterprise system security
and mobile application security. His research inter-
ests also lie in general system and network security,
including but not limited to malware analysis, packet
filters, and Internet data and online game security.
Haining Wang (SM’09) received the Ph.D. degree
in computer science and engineering from the
University of Michigan at Ann Arbor, in 2003.
He is currently a Professor with the Department
of Electrical and Computer Engineering, University
of Delaware. His research interests lie in the area
of security, networking systems, and cloud comput-
ing.
Sushil Jajodia (F’13) received the Ph.D. degree
from the University of Oregon, Eugene. His research
interests include security, privacy, databases, and
distributed systems. He is currently a University
Professor, a BDM International Professor, and the
Founding Director of the Center for Secure Infor-
mation Systems with the Volgenau School of Engi-
neering, George Mason University, Fairfax, VA.