Spammer Detection and Fake User Identification on Social Networks
•Download as DOCX, PDF•
0 likes•94 views
Spammer Detection and Fake User Identification on Social Networks To buy this project in ONLINE, Contact: Email: jpinfotechprojects@gmail.com, Website: https://www.jpinfotech.org
Report
Share
Report
Share
Recommended
Spammer Detection and Fake User Identificationon Social Networks
Spammer Detection and Fake User Identification on Social Networks
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
Spammer detection and fake user Identification on Social Networks
This document summarizes research on detecting spammers and fake users on social networks like Twitter. It presents a taxonomy that classifies techniques for detecting fake content, spam based on URLs, spam in trending topics, and fake users. The techniques are compared based on features like user, content, graph, structure, and time. The goal is to provide researchers a useful overview of recent developments in detecting Twitter spam through different approaches.
Spam identification fake profile
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.
Presentation-Detecting Spammers on Social Networks
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.
Social Media and Scientific Research How Semantic Technologies Enhance Colla...
Darrell Gunter explores the recent trends in social media and networks at the AAP/PSP Annual meeting.
Microposts2015 - Social Spam Detection on Twitter
Making the Most of Tweet-Inherent Features for Social Spam Detection on Twitter
Talk given at #microposts2015, workshop co-located with #www2015
Tarea seminario 3. TIC
This document provides references for a seminar paper on using PubMed to search for articles related to nurse educators' use of wikis, blogs, and other web 2.0 tools for continuing education, networking, and knowledge sharing. The 6 references cited discuss wikis and blogs for continuing nursing education, evaluating an online nursing research network, wikis and blogs as online interaction tools, nurses' adoption of web 2.0 for knowledge sharing and learning, using wikis to enhance networking and learning skills for nursing students, and nursing and healthcare students' experiences with e-learning.
Detailed Research on Fake News: Opportunities, Challenges and Methods
This paper is submitted at Dalhousie University for Technology Innovation course as a deliverable. This paper focuses on the opportunities, challenges and methods for Fake news.
Recommended
Spammer Detection and Fake User Identificationon Social Networks
Spammer Detection and Fake User Identification on Social Networks
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
Spammer detection and fake user Identification on Social Networks
This document summarizes research on detecting spammers and fake users on social networks like Twitter. It presents a taxonomy that classifies techniques for detecting fake content, spam based on URLs, spam in trending topics, and fake users. The techniques are compared based on features like user, content, graph, structure, and time. The goal is to provide researchers a useful overview of recent developments in detecting Twitter spam through different approaches.
Spam identification fake profile
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.
Presentation-Detecting Spammers on Social Networks
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.
Social Media and Scientific Research How Semantic Technologies Enhance Colla...
Darrell Gunter explores the recent trends in social media and networks at the AAP/PSP Annual meeting.
Microposts2015 - Social Spam Detection on Twitter
Making the Most of Tweet-Inherent Features for Social Spam Detection on Twitter
Talk given at #microposts2015, workshop co-located with #www2015
Tarea seminario 3. TIC
This document provides references for a seminar paper on using PubMed to search for articles related to nurse educators' use of wikis, blogs, and other web 2.0 tools for continuing education, networking, and knowledge sharing. The 6 references cited discuss wikis and blogs for continuing nursing education, evaluating an online nursing research network, wikis and blogs as online interaction tools, nurses' adoption of web 2.0 for knowledge sharing and learning, using wikis to enhance networking and learning skills for nursing students, and nursing and healthcare students' experiences with e-learning.
Detailed Research on Fake News: Opportunities, Challenges and Methods
This paper is submitted at Dalhousie University for Technology Innovation course as a deliverable. This paper focuses on the opportunities, challenges and methods for Fake news.
Protecting Your Campus With Shared Intelligence
This document discusses how a shared intelligence system called CIF could help the 64 campuses in the SUNY system collectively share threat data to better protect each other from cyber attacks. CIF would normalize threat data collected from each campus, such as phishing URLs and scanning IPs, and generate security rules and lists that campuses could import into their local systems to more quickly mitigate attacks seen by other campuses. The system is already used successfully by various government and private entities to exchange threat intelligence in a standardized format.
Who You Should Not Follow: Extracting Word Embeddings from Tweets to Identify...
Who You Should Not Follow: Extracting Word Embeddings from Tweets to Identify...JAYAPRAKASH JPINFOTECH
Who You Should Not Follow: Extracting Word Embeddings from Tweets to Identify Groups of Interest and Hijackers in Demonstrations
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.orgFake news detection project
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.
An iac approach for detecting profile cloning
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.
Detecting Malicious Facebook Applications
This article discusses detecting malicious Facebook applications. It presents FRAppE, a tool that can detect malicious apps with 99.5% accuracy. Key points:
- 13% of over 111,000 observed Facebook apps were found to be malicious. Malicious apps often share names and request fewer permissions than benign apps.
- Malicious and benign app profiles differ significantly. Malicious apps exhibit "laziness" - many use the same names. FRAppE uses on-demand and aggregated data to profile apps.
- Apps collude on a massive scale to promote each other. Over 1,500 apps promoted over 3,700 other apps. Well-organized "app-nets" control many malicious apps
identifying malevolent facebook requests
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.
Seminar on detecting fake accounts in social media using machine learning
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.
Retrieving Hidden Friends: A Collusion PrivacyAttack Against Online Friend Se...
Retrieving Hidden Friends: A Collusion PrivacyAttack Against Online Friend Se...JAYAPRAKASH JPINFOTECH
Retrieving Hidden Friends: A Collusion Privacy Attack Against Online Friend Search Engine
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.orgSocial Mar
The document discusses social media research (SMR) as a qualitative research method. SMR involves analyzing existing social media data as the primary data source rather than surveys or focus groups. It has advantages like access to large amounts of natural conversation data over time, but disadvantages like lack of demographic information. The document provides examples of how SMR is used in marketing, social psychology, and academia. Ethical considerations for privacy and interpretation are also discussed.
Library newsletter 2.21
The document summarizes the Summer 2013 issue of a library newsletter. It promotes Mango, an online language learning system with instruction in 34 languages. Users need to create a free Mango profile after logging in with their LMU credentials to access the resources. It also notes that the LMU library location at Cedar Bluff now has 10 laptops available for 4-hour checkout by students, faculty, and staff within the library building. Contact information is provided for library personnel.
A research of software vulnerabilities
This document discusses how software insecurity can be distributed through social networking. It begins with background on software insecurity, which has historically spread through means like floppy disks and early internet sharing. The core topic examines how viruses can propagate through social networking sites as users communicate and share files through features like email, instant messaging, and photo sharing. It also explores how applications and tools used for social networking could host infected content or macros. The document concludes that social networking has become a major means of communication but also enables various ways for software insecurity to spread between users.
PPT.pptx
This document discusses techniques for detecting spam and fake users on social networks like Twitter. It presents a taxonomy that classifies techniques into four categories: detecting fake content, URL-based spam, spam in trending topics, and fake users. The proposed system aims to identify different spam detection approaches on Twitter and classify them using this taxonomy. Features like user features, content features, graph features, structure features, and time features are used to compare techniques. Machine learning methods show effectiveness but dataset availability affects method selection.
A Survey of Methods for Spotting Spammers on Twitter
Social networking sites explosive expansion as a means of information sharing, management, communication, storage, and management has attracted hackers who abuse the Web to take advantage of security flaws for their own nefarious ends. Every day, forged internet accounts are compromised. Online social networks OSNs are rife with impersonators, phishers, scammers, and spammers who are difficult to spot. Users who send unsolicited communications to a large audience with the objective of advertising a product, entice victims to click on harmful links, or infect users systems only for financial gain are known as spammers. Many studies have been conducted to identify spam profiles in OSNs. In this essay, we have discussed the methods currently in use to identify spam Twitter users. User based, content based, or a combination of both features could be used to identify spammers. The current paper gives a summary of the traits, methodologies, detection rates, and restrictions if any for identifying spam profiles, primarily on Twitter. Hareesha Devi | Pankaj Verma | Ankit Dhiman "A Survey of Methods for Spotting Spammers on Twitter" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-3 , June 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd57439.pdf Paper URL: https://www.ijtsrd.com.com/computer-science/artificial-intelligence/57439/a-survey-of-methods-for-spotting-spammers-on-twitter/hareesha-devi
6356152.pdf
Social media websites are becoming more prevalent on the Internet. Sites, such as Twitter, Facebook, and Instagram, spend significantly more of their time on users online. People in social media share thoughts, views, and facts and create new acquaintances. Social media sites supply users with a great deal of useful information. This enormous quantity of social media information invites hackers to abuse data. These hackers establish fraudulent profiles for actual people and distribute useless material. The material on spam might include commercials and harmful URLs that disrupt natural users. This spam content is a massive problem in social networks. Spam identification is a vital procedure on social media networking platforms. In this paper, we have proposed a spam detection artificial intelligence technique for Twitter social networks. In this approach, we employed a vector support machine, a neural artificial network, and a random forest technique to build a model. The results indicate that, compared with RF and ANN algorithms, the suggested support vector machine algorithm has the greatest precision, recall, and Fmeasure. The findings of this paper would be useful in monitoring and tracking social media shared photos for the identification of inappropriate content and forged images and to safeguard social media from digital threats and attacks.
Retrieving Hidden Friends a Collusion Privacy Attack against Online Friend Se...
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
IRJET- Twitter Spammer Detection
This paper proposes a system to detect spammer tweets on Twitter. It extracts features from tweets such as the number of unique mentions, unsolicited mentions, and duplicate domain names. It also uses the Google Safe Browsing API to analyze URLs. The system fetches tweets for a particular hashtag using the Twitter4J API. It then performs preprocessing like removing hashtags and quotes. The features are then used to classify tweets as spam or not spam using machine learning algorithms. The system aims to investigate linguistic features for detecting spam accounts and tweets in a supervised manner by leveraging existing hashtags for training data.
Categorize balanced dataset for troll detection
As we know cyber bullying is increasing day by day and Cyber troll is one of the cyber-aggressive actions that is not much different from cyberbullying in online abuse so that the victims feel uncomfortable. One of the most used social media platforms in which cyber trolling frequently happens is Twitter. Basically, it is found that during an investigation of cyberbullying cases a lot of information gathered is false which aims to give discomfort, hatred and waste lots of time. So, it is necessary to classify between cyberbullying tweets and normal tweets on twitter. There has already been research on classification of cyberbullying tweets and normal tweets using the Support vector machine (SVM) algorithm. But the drawback of the system is that it only gives 63.83% of accuracy. Firstly, we can improve the accuracy of the system by using the Recurrent Neural Network (RNN) And Secondly, for balancing the dataset we will be using Synthetic Minority Over-sampling Technique (SMOTE). We believe that using these techniques we will be able to increase the accuracy of the previous proposed.
Proposal.docx
This document outlines the research methodology for a study on detecting fake profiles in online social networks. It discusses challenges in collecting data from social networks due to privacy and access restrictions. It proposes using an IMcrawler to extract user data from Facebook by scraping profiles. The research will then analyze user behavior and emotions based on collected text data. A fake profile detection model will be developed using profile and network features to identify suspicious connections on Facebook. Classification techniques will be evaluated for the model.
IRJET - Detecting Spiteful Accounts in Social Network
This document discusses detecting spiteful accounts in social networks. It proposes a framework to identify malicious accounts based on features identified in previous research. The framework would analyze posts and URLs to classify accounts as malicious or not without visiting landing pages. The architecture describes the detection mechanism analyzing content during posting to flag malicious posts which are sent directly to administrators. Previous research on classifying applications and events on social networks is discussed. The proposed system aims to easily identify malicious posts and users while maintaining database of malicious accounts and upholding social network values.
Dynamic feature selection for spam detection (1).pptx
1) The document proposes a dynamic feature selection technique for classifying spam users on Twitter that uses different feature sets for different user groups rather than a static feature set.
2) Data is collected using a custom crawler to gather user information and features like user attributes, content attributes, and social network features are extracted.
3) Machine learning algorithms like k-NN and SVM are applied to the feature sets to classify users as spam or not spam based on their group.
Spammer Detection and Fake User Identification on Social Networks
This document discusses methods for detecting spammers and fake users on social networks like Twitter. It provides a literature review of past research on spam detection techniques on Twitter. The techniques are categorized based on their ability to detect: (1) fake content, (2) spam based on URLs, (3) spam in popular topics, and (4) fake users. The techniques are also analyzed based on attributes like user attributes, content attributes, graph attributes, structural attributes, and time attributes. An implementation approach is proposed that involves collecting user data, employing machine learning algorithms like random forest for pattern recognition, using feature engineering, and integrating social media APIs for real-time monitoring. It also discusses integrating a user reporting mechanism to improve accuracy
Terrorism Analysis through Social Media using Data Mining
This document presents a study that uses deep learning models like Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN) to analyze terrorism through detecting toxicity in social media text data. The study aims to classify text data into categories like toxicity, severe toxicity, obscenity, threat, insult or identity hate. It provides an overview of DNN and CNN models for text classification and compares their methodology, architecture and performance. The models are trained on preprocessed social media data related to terrorist activities and aim to accurately predict the toxicity level and classify tweets for concerned authorities to make informed decisions.
More Related Content
What's hot
Protecting Your Campus With Shared Intelligence
This document discusses how a shared intelligence system called CIF could help the 64 campuses in the SUNY system collectively share threat data to better protect each other from cyber attacks. CIF would normalize threat data collected from each campus, such as phishing URLs and scanning IPs, and generate security rules and lists that campuses could import into their local systems to more quickly mitigate attacks seen by other campuses. The system is already used successfully by various government and private entities to exchange threat intelligence in a standardized format.
Who You Should Not Follow: Extracting Word Embeddings from Tweets to Identify...
Who You Should Not Follow: Extracting Word Embeddings from Tweets to Identify...JAYAPRAKASH JPINFOTECH
Who You Should Not Follow: Extracting Word Embeddings from Tweets to Identify Groups of Interest and Hijackers in Demonstrations
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.orgFake news detection project
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.
An iac approach for detecting profile cloning
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.
Detecting Malicious Facebook Applications
This article discusses detecting malicious Facebook applications. It presents FRAppE, a tool that can detect malicious apps with 99.5% accuracy. Key points:
- 13% of over 111,000 observed Facebook apps were found to be malicious. Malicious apps often share names and request fewer permissions than benign apps.
- Malicious and benign app profiles differ significantly. Malicious apps exhibit "laziness" - many use the same names. FRAppE uses on-demand and aggregated data to profile apps.
- Apps collude on a massive scale to promote each other. Over 1,500 apps promoted over 3,700 other apps. Well-organized "app-nets" control many malicious apps
identifying malevolent facebook requests
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.
Seminar on detecting fake accounts in social media using machine learning
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.
Retrieving Hidden Friends: A Collusion PrivacyAttack Against Online Friend Se...
Retrieving Hidden Friends: A Collusion PrivacyAttack Against Online Friend Se...JAYAPRAKASH JPINFOTECH
Retrieving Hidden Friends: A Collusion Privacy Attack Against Online Friend Search Engine
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.orgSocial Mar
The document discusses social media research (SMR) as a qualitative research method. SMR involves analyzing existing social media data as the primary data source rather than surveys or focus groups. It has advantages like access to large amounts of natural conversation data over time, but disadvantages like lack of demographic information. The document provides examples of how SMR is used in marketing, social psychology, and academia. Ethical considerations for privacy and interpretation are also discussed.
Library newsletter 2.21
The document summarizes the Summer 2013 issue of a library newsletter. It promotes Mango, an online language learning system with instruction in 34 languages. Users need to create a free Mango profile after logging in with their LMU credentials to access the resources. It also notes that the LMU library location at Cedar Bluff now has 10 laptops available for 4-hour checkout by students, faculty, and staff within the library building. Contact information is provided for library personnel.
A research of software vulnerabilities
This document discusses how software insecurity can be distributed through social networking. It begins with background on software insecurity, which has historically spread through means like floppy disks and early internet sharing. The core topic examines how viruses can propagate through social networking sites as users communicate and share files through features like email, instant messaging, and photo sharing. It also explores how applications and tools used for social networking could host infected content or macros. The document concludes that social networking has become a major means of communication but also enables various ways for software insecurity to spread between users.
What's hot (11)
Who You Should Not Follow: Extracting Word Embeddings from Tweets to Identify...
Who You Should Not Follow: Extracting Word Embeddings from Tweets to Identify...
Seminar on detecting fake accounts in social media using machine learning
Seminar on detecting fake accounts in social media using machine learning
Retrieving Hidden Friends: A Collusion PrivacyAttack Against Online Friend Se...
Retrieving Hidden Friends: A Collusion PrivacyAttack Against Online Friend Se...
Similar to Spammer Detection and Fake User Identification on Social Networks
PPT.pptx
This document discusses techniques for detecting spam and fake users on social networks like Twitter. It presents a taxonomy that classifies techniques into four categories: detecting fake content, URL-based spam, spam in trending topics, and fake users. The proposed system aims to identify different spam detection approaches on Twitter and classify them using this taxonomy. Features like user features, content features, graph features, structure features, and time features are used to compare techniques. Machine learning methods show effectiveness but dataset availability affects method selection.
A Survey of Methods for Spotting Spammers on Twitter
Social networking sites explosive expansion as a means of information sharing, management, communication, storage, and management has attracted hackers who abuse the Web to take advantage of security flaws for their own nefarious ends. Every day, forged internet accounts are compromised. Online social networks OSNs are rife with impersonators, phishers, scammers, and spammers who are difficult to spot. Users who send unsolicited communications to a large audience with the objective of advertising a product, entice victims to click on harmful links, or infect users systems only for financial gain are known as spammers. Many studies have been conducted to identify spam profiles in OSNs. In this essay, we have discussed the methods currently in use to identify spam Twitter users. User based, content based, or a combination of both features could be used to identify spammers. The current paper gives a summary of the traits, methodologies, detection rates, and restrictions if any for identifying spam profiles, primarily on Twitter. Hareesha Devi | Pankaj Verma | Ankit Dhiman "A Survey of Methods for Spotting Spammers on Twitter" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-3 , June 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd57439.pdf Paper URL: https://www.ijtsrd.com.com/computer-science/artificial-intelligence/57439/a-survey-of-methods-for-spotting-spammers-on-twitter/hareesha-devi
6356152.pdf
Social media websites are becoming more prevalent on the Internet. Sites, such as Twitter, Facebook, and Instagram, spend significantly more of their time on users online. People in social media share thoughts, views, and facts and create new acquaintances. Social media sites supply users with a great deal of useful information. This enormous quantity of social media information invites hackers to abuse data. These hackers establish fraudulent profiles for actual people and distribute useless material. The material on spam might include commercials and harmful URLs that disrupt natural users. This spam content is a massive problem in social networks. Spam identification is a vital procedure on social media networking platforms. In this paper, we have proposed a spam detection artificial intelligence technique for Twitter social networks. In this approach, we employed a vector support machine, a neural artificial network, and a random forest technique to build a model. The results indicate that, compared with RF and ANN algorithms, the suggested support vector machine algorithm has the greatest precision, recall, and Fmeasure. The findings of this paper would be useful in monitoring and tracking social media shared photos for the identification of inappropriate content and forged images and to safeguard social media from digital threats and attacks.
Retrieving Hidden Friends a Collusion Privacy Attack against Online Friend Se...
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
IRJET- Twitter Spammer Detection
This paper proposes a system to detect spammer tweets on Twitter. It extracts features from tweets such as the number of unique mentions, unsolicited mentions, and duplicate domain names. It also uses the Google Safe Browsing API to analyze URLs. The system fetches tweets for a particular hashtag using the Twitter4J API. It then performs preprocessing like removing hashtags and quotes. The features are then used to classify tweets as spam or not spam using machine learning algorithms. The system aims to investigate linguistic features for detecting spam accounts and tweets in a supervised manner by leveraging existing hashtags for training data.
Categorize balanced dataset for troll detection
As we know cyber bullying is increasing day by day and Cyber troll is one of the cyber-aggressive actions that is not much different from cyberbullying in online abuse so that the victims feel uncomfortable. One of the most used social media platforms in which cyber trolling frequently happens is Twitter. Basically, it is found that during an investigation of cyberbullying cases a lot of information gathered is false which aims to give discomfort, hatred and waste lots of time. So, it is necessary to classify between cyberbullying tweets and normal tweets on twitter. There has already been research on classification of cyberbullying tweets and normal tweets using the Support vector machine (SVM) algorithm. But the drawback of the system is that it only gives 63.83% of accuracy. Firstly, we can improve the accuracy of the system by using the Recurrent Neural Network (RNN) And Secondly, for balancing the dataset we will be using Synthetic Minority Over-sampling Technique (SMOTE). We believe that using these techniques we will be able to increase the accuracy of the previous proposed.
Proposal.docx
This document outlines the research methodology for a study on detecting fake profiles in online social networks. It discusses challenges in collecting data from social networks due to privacy and access restrictions. It proposes using an IMcrawler to extract user data from Facebook by scraping profiles. The research will then analyze user behavior and emotions based on collected text data. A fake profile detection model will be developed using profile and network features to identify suspicious connections on Facebook. Classification techniques will be evaluated for the model.
IRJET - Detecting Spiteful Accounts in Social Network
This document discusses detecting spiteful accounts in social networks. It proposes a framework to identify malicious accounts based on features identified in previous research. The framework would analyze posts and URLs to classify accounts as malicious or not without visiting landing pages. The architecture describes the detection mechanism analyzing content during posting to flag malicious posts which are sent directly to administrators. Previous research on classifying applications and events on social networks is discussed. The proposed system aims to easily identify malicious posts and users while maintaining database of malicious accounts and upholding social network values.
Dynamic feature selection for spam detection (1).pptx
1) The document proposes a dynamic feature selection technique for classifying spam users on Twitter that uses different feature sets for different user groups rather than a static feature set.
2) Data is collected using a custom crawler to gather user information and features like user attributes, content attributes, and social network features are extracted.
3) Machine learning algorithms like k-NN and SVM are applied to the feature sets to classify users as spam or not spam based on their group.
Spammer Detection and Fake User Identification on Social Networks
This document discusses methods for detecting spammers and fake users on social networks like Twitter. It provides a literature review of past research on spam detection techniques on Twitter. The techniques are categorized based on their ability to detect: (1) fake content, (2) spam based on URLs, (3) spam in popular topics, and (4) fake users. The techniques are also analyzed based on attributes like user attributes, content attributes, graph attributes, structural attributes, and time attributes. An implementation approach is proposed that involves collecting user data, employing machine learning algorithms like random forest for pattern recognition, using feature engineering, and integrating social media APIs for real-time monitoring. It also discusses integrating a user reporting mechanism to improve accuracy
Terrorism Analysis through Social Media using Data Mining
This document presents a study that uses deep learning models like Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN) to analyze terrorism through detecting toxicity in social media text data. The study aims to classify text data into categories like toxicity, severe toxicity, obscenity, threat, insult or identity hate. It provides an overview of DNN and CNN models for text classification and compares their methodology, architecture and performance. The models are trained on preprocessed social media data related to terrorist activities and aim to accurately predict the toxicity level and classify tweets for concerned authorities to make informed decisions.
Internet Essay
The document discusses the history and development of the Internet. It began as a project called ARPANET in the 1950s by the U.S. Department of Defense to link computers and allow for information sharing. Through the 1970s, protocols like TCP/IP were developed and networks expanded. The World Wide Web was created in 1989 by Tim Berners-Lee, allowing for easy information sharing through hyperlinks on Internet-connected computers. The Internet has since revolutionized communication, business, education and more.
IRJET - Real-Time Cyberbullying Analysis on Social Media using Machine Learni...
This document presents a system for real-time analysis of cyberbullying on social media using machine learning and text mining. The system aims to detect abusive conversations and censor harmful words to protect victims. It uses an artificial neural network machine learning algorithm to analyze words that could psychologically affect individuals. The system identifies abusive words in posts and comments and replaces them with censored content. This aims to prevent innocent users from being exposed to depressing or criminal activities online. The document discusses the system architecture, including tools for sentiment analysis, monitoring discussions, identifying abusive words, and updating a word database. Diagrams show the data flow, use cases, and interactions between system components.
EXPLORATORY DATA ANALYSIS AND FEATURE SELECTION FOR SOCIAL MEDIA HACKERS PRED...
In machine learning, the intelligence of a developed model is greatly influenced by the dataset used for the
target domain on which the developed model will be deployed. Social media platform has experienced
more of hackers’ attacks on the platform in recent time. To identify a hacker on the platform, there are two
possible ways. The first is to use the activities of the user while the second is to use the supplied details the
user registered the account with. To adequately identify a social media user as hacker proactively, there
are relevant user details called features that can be used to determine whether a social media user is a
hacker or not. In this paper, an exploratory data analysis was carried out to determine the best features
that can be used by a predictive model to proactively identify hackers on the social media platform. A web
crawler was developed to mine the user dataset on which exploratory data analysis was carried out to
select the best features for the dataset which could be used to correctly identify a hacker on a social media
platform.
Exploratory Data Analysis and Feature Selection for Social Media Hackers Pred...
In machine learning, the intelligence of a developed model is greatly influenced by the dataset used for the
target domain on which the developed model will be deployed. Social media platform has experienced
more of hackers’ attacks on the platform in recent time. To identify a hacker on the platform, there are two
possible ways. The first is to use the activities of the user while the second is to use the supplied details the
user registered the account with. To adequately identify a social media user as hacker proactively, there
are relevant user details called features that can be used to determine whether a social media user is a
hacker or not. In this paper, an exploratory data analysis was carried out to determine the best features
that can be used by a predictive model to proactively identify hackers on the social media platform. A web
crawler was developed to mine the user dataset on which exploratory data analysis was carried out to
select the best features for the dataset which could be used to correctly identify a hacker on a social media
platform.
Exploring machine learning techniques for fake profile detection in online so...
The online social network is the largest network, more than 4 billion users use social media and with its rapid growth, the risk of maintaining the integrity of data has tremendously increased. There are several kinds of security challenges in online social networks (OSNs). Many abominable behaviors try to hack social sites and misuse the data available on these sites. Therefore, protection against such behaviors has become an essential requirement. Though there are many types of security threats in online social networks but, one of the significant threats is the fake profile. Fake profiles are created intentionally with certain motives, and such profiles may be targeted to steal or acquire sensitive information and/or spread rumors on online social networks with specific motives. Fake profiles are primarily used to steal or extract information by means of friendly interaction online and/or misusing online data available on social sites. Thus, fake profile detection in social media networks is attracting the attention of researchers. This paper aims to discuss various machine learning (ML) methods used by researchers for fake profile detection to explore the further possibility of improvising the machine learning models for speedy results.
Botnet Detection in Online-social Network
Botnet, Bot master, Command and Control Server, States for Bots, Types of attacks, most wanted bots, Botnet life cycle, botnet topology, Social botnet.
A CONCEPTUAL FRAMEWORK OF A DETECTIVE MODEL FOR SOCIAL BOT CLASSIFICATION
Social media platform has greatly enhanced human interactive activities in the virtual community. Virtual
socialization has positively influenced social bonding among social media users irrespective of one’s
location in the connected global village. Human user and social bot user are the two types of social media
users. While human users personally operate their social media accounts, social bot users are developed
software that manages a social media account for the human user called the botmaster. This botmaster in
most cases are hackers with bad intention of attacking social media users through various attacking mode
using social bots. The aim of this research work is to design an intelligent framework that will prevent
attacks through social bots on social media network platforms.
V01 i010413
SQL Vulnerability Prevention in Cybercrime using Dynamic Evaluation of Shell and Remote File Injection Attacks R. Ravi,
Department of Computer Science & Engineering,
Francis Xavier Engineering College, Tamil Nadu, India
Dr. Beulah Shekhar,
Department of Criminology,
Manonmanium Sundaranar University, Tamil Nadu, India
Identifying Malicious Data in Social Media
This document discusses two approaches for identifying malicious data in social media: Shannon entropy and power law distribution. The Shannon entropy approach calculates the entropy of features like source/destination IP addresses and port numbers to detect anomalous network traffic patterns. The power law distribution approach models malware propagation across networks and finds that malware distribution transitions from exponential to power law over time. Experimental results on social media datasets found the Shannon entropy approach could detect malware based on the number of applications installed, while power law distribution identified good and malicious files shared between users. Both techniques aim to improve detection of malicious content shared over social networks.
Similar to Spammer Detection and Fake User Identification on Social Networks (20)
A Survey of Methods for Spotting Spammers on Twitter
A Survey of Methods for Spotting Spammers on Twitter
Retrieving Hidden Friends a Collusion Privacy Attack against Online Friend Se...
Retrieving Hidden Friends a Collusion Privacy Attack against Online Friend Se...
IRJET - Detecting Spiteful Accounts in Social Network
IRJET - Detecting Spiteful Accounts in Social Network
Dynamic feature selection for spam detection (1).pptx
Dynamic feature selection for spam detection (1).pptx
Spammer Detection and Fake User Identification on Social Networks
Spammer Detection and Fake User Identification on Social Networks
Terrorism Analysis through Social Media using Data Mining
Terrorism Analysis through Social Media using Data Mining
IRJET - Real-Time Cyberbullying Analysis on Social Media using Machine Learni...
IRJET - Real-Time Cyberbullying Analysis on Social Media using Machine Learni...
EXPLORATORY DATA ANALYSIS AND FEATURE SELECTION FOR SOCIAL MEDIA HACKERS PRED...
EXPLORATORY DATA ANALYSIS AND FEATURE SELECTION FOR SOCIAL MEDIA HACKERS PRED...
Exploratory Data Analysis and Feature Selection for Social Media Hackers Pred...
Exploratory Data Analysis and Feature Selection for Social Media Hackers Pred...
Exploring machine learning techniques for fake profile detection in online so...
Exploring machine learning techniques for fake profile detection in online so...
A CONCEPTUAL FRAMEWORK OF A DETECTIVE MODEL FOR SOCIAL BOT CLASSIFICATION
A CONCEPTUAL FRAMEWORK OF A DETECTIVE MODEL FOR SOCIAL BOT CLASSIFICATION
More from JAYAPRAKASH JPINFOTECH
Java Web Application Project Titles 2023-2024.pdf
Java Web Application Project Titles 2023-2024.
🔗Email: jpinfotechprojects@gmail.com,
🌐Website: https://www.jpinfotech.org
📞MOBILE: (+91)9952649690.
Java Application Projects 2023 - 2024
Java Web Application Project Titles
E-Authentication System using QR Code and OTP
Student Attendance System Using QR-Code
Hall Ticket Generation System with Integrated QR Code
Certificate Authentication System using QR Code
QR Code-based Smart Vehicle Parking Management System
Employee Attendance System using QR Code
QR Code based Secure Online Voting System
QR Code Based Smart Online Student Attendance System
Cyber Security Projects
Detecting Malicious Facebook Applications
Detection of Bullying Messages in Social Media
Enhanced Secure Login System using Captcha as Graphical Passwords
Filtering Unwanted Messages in Online Social Networking User walls
Secure Online Transaction System with Cryptography
Detecting Mobile Malicious Webpages in Real Time
Credit Card Fraud Detection in Online Shopping System
Enhanced Data Security with Onion Encryption and Key Rotation
Detection of Offensive Messages in Social Media to Protect Online Safety
Healthcare Projects
Diabetes Prediction using Data Mining in Healthcare Management System
Online Hospital Management System
Online Oxygen Management System
Enhanced Hospital Admission System to Mitigate Crowding
Online Parking Booking System
E-Pass Management System | Curfew e-pass management system
Online Tender Management System
Online Toll Gate Management System
Online Election System
Panchayat Union Automation System
Smart City Project - A Complete City Guide Using Database
Visa Processing Management System
Cricket Win Predictor using Machine Learning
College Management System
Online college Counselling system
Online No Dues Management System
Online Student Mentoring System
Online Tuition Management System
Bike Store Management System
Computer Inventory System
Distilled Water Management System
Donation Tracking System | Online Charity Management System
Online Bug Tracking System
Online Content Based Image Retrieval System with Ranking Model
Online Crime File Management System
Online Courier Management System
Online Blood Bank Management System
Online Secure Organ Donation Management System
Connecting Social Media to E-Commerce
Twitter Based Tweet Summarization
Mental Disorders Detection via Online Social Media Mining
Detecting Stress Based on Social Interactions in Social Networks
Knowledge Sharing Based Online Social Network with Question and Answering System
Predicting Suicide Intuition in Online Social Network
Predicting Emotions of User in Online Social Network
Employee Payroll Management System
Human Resource Management System
Online Employee Tracking System
College Admission Predictor
Online Book Recommendation System
Personalized Movie Recommendation System
Product Recommendation System in Online Social Network
Mining Online Product Evaluation System based on Ratings and Review Comments
Online Book Buying and Selling
Dot Net Final Year IEEE Project Titles.pdf
Dot Net Final Year IEEE Project Titles.pdf
🔗Email: jpinfotechprojects@gmail.com,
🌐Website: https://www.jpinfotech.org
📞MOBILE: (+91)9952649690.
MATLAB Final Year IEEE Project Titles 2023 - 2024.pdf
The document provides details about MATLAB final year projects for 2023-2024 in various domains including medical image processing, face recognition, facial expression analysis, agriculture, transportation systems, biometrics, object detection and recognition, and data hiding/steganography. It lists 25 MATLAB projects related to deep learning and image processing with project codes and titles, domains, algorithms/methods used, and programming language/year. It also provides contact information for the organization providing these project ideas.
Python IEEE Project Titles 2023 - 2024.pdf
Python IEEE Papers / Projects 2023 – 2024.
🔗Email: jpinfotechprojects@gmail.com,
🌐Website: https://www.jpinfotech.org
📞MOBILE: (+91)9952649690.
DEEP LEARNING IEEE PROJECTS 2023
Blood Cancer Identification using Hybrid Ensemble Deep Learning Technique
Breast Cancer Classification using CNN with Transfer Learning Models
Calorie Estimation of Food and Beverages using Deep Learning
Detection and Identification of Pills using Machine Learning Models
Detection of Cardiovascular Diseases in ECG Images Using Machine Learning and Deep Learning Methods
Development of Hybrid Image Caption Generation Method using Deep Learning
Dog Breed Classification using Inception-ResNet-V2
Forest Fire Detection using Convolutional Neural Networks (CNN)
Digital Image Forgery Detection Using Deep Learning
Image-Based Bird Species Identification Using Machine Learning
Kidney Cancer Detection using Deep Learning Models
Medicinal Herbs Identification
Monkeypox Diagnosis with Interpretable Deep Learning
Music Genre Classification Using Convolutional Neural Network
Pancreatic Cancer Classification using Deep Learning
Prediction of Lung Cancer using Convolution Neural Networks
Signature Fraud Detection using Deep Learning
Skin Cancer Prediction Using Deep Learning Techniques
Traffic Sign Classification using Deep Learning
Disease Classification in Wheat from Images Using CNN
Detection of Lungs Cancer through Computed Tomographic Images using Deep Learning
MACHINE LEARNING IEEE PROJECTS 2023
A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders
A Machine Learning Model to Predict a Diagnosis of Brain Stroke
CO2 Emission Rating by Vehicles Using Data Science
Cyber Hacking Breaches Prediction and Detection Using Machine Learning
Fake Profile Detection on Social Networking Websites using Machine Learning
Crime Prediction Using Machine Learning and Deep Learning
Drug Recommendation System in Medical Emergencies using Machine Learning
Efficient Machine Learning Algorithm for Future Gold Price Prediction
Heart Disease Prediction With Machine Learning
House Price Prediction using Machine Learning Algorithm
Human Stress Detection Based on Sleeping Habits Using Machine Learning Algorithms
Python ieee project titles 2021 - 2022 | Machine Learning Final Year Project...
Python ieee project titles 2021 - 2022 | Machine Learning Final Year Project...JAYAPRAKASH JPINFOTECH
Python ieee project titles 2021 - 2022 | Machine Learning Final Year Projects 2021 - 2022
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
python projects,
python projects for beginners,
python projects with source code,
python projects source code,
python projects beginners,
python projects in github,
python projects ideas,
python projects github,
python projects on github,
python projects for beginners with code,
python projects simple,
python projects for resume,
python projects for beginners with source code,
python projects for class 12,
python projects for class 12 with source code,
python projects with source code github,
python projects list,
python projects intermediate,
python projects sample,
python projects with database,
python projects to practice,
python projects for intermediate,
python projects practice,
python project code,
Sentiment Classification using N-gram IDF and Automated Machine Learning
Sentiment Classification using N-gram IDF and Automated Machine Learning
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
Privacy-Preserving Social Media DataPublishing for Personalized Ranking-Based...
Privacy-Preserving Social Media DataPublishing for Personalized Ranking-Based...JAYAPRAKASH JPINFOTECH
Privacy-Preserving Social Media Data Publishing for Personalized Ranking-Based Recommendation
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.orgFunkR-pDAE: Personalized Project Recommendation Using Deep Learning
FunkR-pDAE: Personalized Project Recommendation Using Deep Learning
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
Discovering the Type 2 Diabetes in Electronic Health Records using the Sparse...
Discovering the Type 2 Diabetes in Electronic Health Records using the Sparse...JAYAPRAKASH JPINFOTECH
Discovering the Type 2 Diabetes in Electronic Health Records using the Sparse Balanced Support Vector Machine
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.orgCrop Yield Prediction and Efficient use of Fertilizers
Crop Yield Prediction and Efficient use of Fertilizers
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
Collaborative Filtering-based Electricity Plan Recommender System
Collaborative Filtering-based Electricity Plan Recommender System
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
Achieving Data Truthfulness and Privacy Preservation in Data Markets
Achieving Data Truthfulness and Privacy Preservation in Data Markets
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
V2V Routing in a VANET Based on the Auto regressive Integrated Moving Average...
V2V Routing in a VANET Based on the Auto regressive Integrated Moving Average...JAYAPRAKASH JPINFOTECH
V2V Routing in a VANET Based on the Auto regressive Integrated Moving Average Model
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.orgTowards Fast and Reliable Multi-hop Routing in VANETs
The document proposes a new multi-hop broadcasting protocol called the Intelligent Forwarding Protocol (IFP) for disseminating safety messages in vehicular ad-hoc networks (VANETs). IFP exploits handshake-less communication, ACK decoupling, and efficient collision resolution to significantly reduce message propagation delays and improve packet delivery ratios compared to existing schemes. The paper presents an in-depth analysis and optimization of IFP using theoretical modeling, simulations, and real-world experimentation.
Selective Authentication Based Geographic Opportunistic Routing in Wireless S...
Selective Authentication Based Geographic Opportunistic Routing in Wireless S...JAYAPRAKASH JPINFOTECH
This document proposes a selective authentication-based geographic opportunistic routing (SelGOR) for wireless sensor networks used in IoT applications. SelGOR aims to guarantee reliable data delivery over unstable wireless links while defending against DoS attacks. It analyzes statistical state information to improve routing efficiency and develops an entropy-based selective authentication algorithm to ensure data integrity and isolate attackers. Simulations show SelGOR provides reliable and authentic data delivery with 50% lower computational cost than other related solutions.Robust Defense Scheme Against Selective DropAttack in Wireless Ad Hoc Networks
Robust Defense Scheme Against Selective DropAttack in Wireless Ad Hoc NetworksJAYAPRAKASH JPINFOTECH
Robust Defense Scheme Against Selective DropAttack in Wireless Ad Hoc Networks
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.orgPrivacy-Preserving Cloud-based Road Condition Monitoring with Source Authenti...
Privacy-Preserving Cloud-based Road Condition Monitoring with Source Authenti...JAYAPRAKASH JPINFOTECH
Privacy-Preserving Cloud-based Road Condition Monitoring with Source Authentication in VANETs
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.orgNovel Intrusion Detection and Prevention for Mobile Ad Hoc Networks
Novel Intrusion Detection and Prevention for Mobile Ad Hoc Networks
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
Node-Level Trust Evaluation in Wireless Sensor Networks
Node-Level Trust Evaluation in Wireless Sensor Networks
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
New Multi-Hop Clustering Algorithm for Vehicular Ad Hoc Networks
The document proposes a new multi-hop clustering algorithm (PMC) for vehicular ad hoc networks (VANETs) to improve routing performance and stability. The PMC selects cluster heads based on node mobility and priority within an N-hop range to increase reliability and stability of clusters. It introduces a cluster merging mechanism to further enhance reliability and robustness. Experiments show PMC outperforms existing algorithms like N-HOP, VMaSC, and DMCNF in stability and reliability for VANET clustering.
More from JAYAPRAKASH JPINFOTECH (20)
MATLAB Final Year IEEE Project Titles 2023 - 2024.pdf
MATLAB Final Year IEEE Project Titles 2023 - 2024.pdf
Python ieee project titles 2021 - 2022 | Machine Learning Final Year Project...
Python ieee project titles 2021 - 2022 | Machine Learning Final Year Project...
Sentiment Classification using N-gram IDF and Automated Machine Learning
Sentiment Classification using N-gram IDF and Automated Machine Learning
Privacy-Preserving Social Media DataPublishing for Personalized Ranking-Based...
Privacy-Preserving Social Media DataPublishing for Personalized Ranking-Based...
FunkR-pDAE: Personalized Project Recommendation Using Deep Learning
FunkR-pDAE: Personalized Project Recommendation Using Deep Learning
Discovering the Type 2 Diabetes in Electronic Health Records using the Sparse...
Discovering the Type 2 Diabetes in Electronic Health Records using the Sparse...
Crop Yield Prediction and Efficient use of Fertilizers
Crop Yield Prediction and Efficient use of Fertilizers
Collaborative Filtering-based Electricity Plan Recommender System
Collaborative Filtering-based Electricity Plan Recommender System
Achieving Data Truthfulness and Privacy Preservation in Data Markets
Achieving Data Truthfulness and Privacy Preservation in Data Markets
V2V Routing in a VANET Based on the Auto regressive Integrated Moving Average...
V2V Routing in a VANET Based on the Auto regressive Integrated Moving Average...
Towards Fast and Reliable Multi-hop Routing in VANETs
Towards Fast and Reliable Multi-hop Routing in VANETs
Selective Authentication Based Geographic Opportunistic Routing in Wireless S...
Selective Authentication Based Geographic Opportunistic Routing in Wireless S...
Robust Defense Scheme Against Selective DropAttack in Wireless Ad Hoc Networks
Robust Defense Scheme Against Selective DropAttack in Wireless Ad Hoc Networks
Privacy-Preserving Cloud-based Road Condition Monitoring with Source Authenti...
Privacy-Preserving Cloud-based Road Condition Monitoring with Source Authenti...
Novel Intrusion Detection and Prevention for Mobile Ad Hoc Networks
Novel Intrusion Detection and Prevention for Mobile Ad Hoc Networks
Node-Level Trust Evaluation in Wireless Sensor Networks
Node-Level Trust Evaluation in Wireless Sensor Networks
New Multi-Hop Clustering Algorithm for Vehicular Ad Hoc Networks
New Multi-Hop Clustering Algorithm for Vehicular Ad Hoc Networks
Recently uploaded
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Aberdeen
Chapter wise All Notes of First year Basic Civil Engineering.pptx
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh
Leveraging Generative AI to Drive Nonprofit Innovation
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.ppt
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
HYPERTENSION - SLIDE SHARE PRESENTATION.
IT WILL BE HELPFULL FOR THE NUSING STUDENTS
IT FOCUSED ON MEDICAL MANAGEMENT AND NURSING MANAGEMENT.
HIGHLIGHTS ON HEALTH EDUCATION.
How to Make a Field Mandatory in Odoo 17
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
Practical manual for National Examination Council, Nigeria.
Contains guides on answering questions on the specimens provided
Recently uploaded (20)
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
Chapter wise All Notes of First year Basic Civil Engineering.pptx
Chapter wise All Notes of First year Basic Civil Engineering.pptx
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
REASIGNACION 2024 UGEL CHUPACA 2024 UGEL CHUPACA.pdf
REASIGNACION 2024 UGEL CHUPACA 2024 UGEL CHUPACA.pdf
Film vocab for eal 3 students: Australia the movie
Film vocab for eal 3 students: Australia the movie
Leveraging Generative AI to Drive Nonprofit Innovation
Leveraging Generative AI to Drive Nonprofit Innovation
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
SWOT analysis in the project Keeping the Memory @live.pptx
SWOT analysis in the project Keeping the Memory @live.pptx
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.ppt
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.ppt
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
Spammer Detection and Fake User Identification on Social Networks
- 1. Spammer Detection and Fake User Identification on Social Networks ABSTRACT: Social networking sites engage millions of users around the world. The users' interactions with these social sites, such as Twitter and Facebook have a tremendous impact and occasionally undesirable repercussions for daily life. The prominent social networking sites have turned into a target platform for the spammers to disperse a huge amount of irrelevant and deleterious information. Twitter, for example, has become one of the most extravagantly used platforms of all times and therefore allows an unreasonable amount of spam. Fake users send undesired tweets to users to promote services or websites that not only affect legitimate users but also disrupt resource consumption. Moreover, the possibility of expanding invalid information to users through fake identities has increased that results in the unrolling of harmful content. Recently, the detection of spammers and identification of fake users on Twitter has become a common area of research in contemporary online social Networks (OSNs). In this paper, we perform a review of techniques used for detecting spammers on Twitter. Moreover, a taxonomy of the Twitter spam detection approaches is presented that classifies the techniques based on their ability to detect: (i) fake content, (ii) spam based on URL, (iii) spam in trending topics, and (iv) fake users. The presented techniques are also compared based on various features, such as user features, content features, graph features, structure features, and time features. We are hopeful that
- 2. the presented study will be a useful resource for researchers to find the highlights of recent developments in Twitter spam detection on a single platform. SYSTEM REQUIREMENTS: HARDWARE REQUIREMENTS: System : Pentium Dual Core. Hard Disk : 120 GB. Monitor : 15’’ LED Input Devices : Keyboard, Mouse Ram : 1 GB SOFTWARE REQUIREMENTS: Operating system : Windows 7. Coding Language : JAVA. Tool : Netbeans 7.2.1 Database : MYSQL REFERENCE: FAIZA MASOOD1, GHANA AMMAD1, AHMAD ALMOGREN 2, (Senior Member, IEEE), ASSAD ABBAS 1, HASAN ALI KHATTAK 1, (Senior Member, IEEE), IKRAM UD DIN 3, (Senior Member, IEEE), MOHSEN
- 3. GUIZANI 4, (Fellow, IEEE), AND MANSOUR ZUAIR5, “Spammer Detection and Fake User Identification on Social Networks”, IEEE Access, 2019.