Spammer Detection and Fake User Identificationon Social Networks
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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
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Spammer Detection and Fake User Identification on 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.
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Who You Should Not Follow: Extracting Word Embeddings from Tweets to Identify...JAYAPRAKASH JPINFOTECH
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To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.orgFRAppE Detecting Malicious Facebook Applications
The document presents FRAppE, a tool for detecting malicious Facebook applications. FRAppE analyzes data from 111,000 Facebook applications seen across 2.2 million users to identify distinguishing features between malicious and benign apps. Using these features, FRAppE can detect malicious apps with 99.5% accuracy while having no false positives and a low false negative rate of 4.1%. The document also explores how malicious apps collude to propagate across Facebook by enabling the viral spread of other malicious apps through posts.
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.
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Spammer Detection and Fake User Identification on 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.
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Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.orgFRAppE Detecting Malicious Facebook Applications
The document presents FRAppE, a tool for detecting malicious Facebook applications. FRAppE analyzes data from 111,000 Facebook applications seen across 2.2 million users to identify distinguishing features between malicious and benign apps. Using these features, FRAppE can detect malicious apps with 99.5% accuracy while having no false positives and a low false negative rate of 4.1%. The document also explores how malicious apps collude to propagate across Facebook by enabling the viral spread of other malicious apps through posts.
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.
Fr app e detecting malicious facebook applications
FRAppE is a tool that detects malicious Facebook applications with 99.5% accuracy. It identifies distinguishing features between malicious and benign apps such as malicious apps sharing names and requesting fewer permissions. FRAppE analyzes 111K apps and finds that at least 13% are malicious. It also finds massive collusion between apps, with some promoting thousands of other apps. This indicates organized crime rings controlling many malicious apps, termed "AppNets".
Fr app e detecting malicious facebook applications
We are the company providing Complete Solution for all Academic Final Year/Semester Student Projects. Our projects are
suitable for B.E (CSE,IT,ECE,EEE), B.Tech (CSE,IT,ECE,EEE),M.Tech (CSE,IT,ECE,EEE) B.sc (IT & CSE), M.sc (IT & CSE),
MCA, and many more..... We are specialized on Java,Dot Net ,PHP & Andirod technologies. Each Project listed comes with
the following deliverable: 1. Project Abstract 2. Complete functional code 3. Complete Project report with diagrams 4.
Database 5. Screen-shots 6. Video File
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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.
DETECTING MALICIOUS FACEBOOK APPLICATIONS - IEEE PROJECTS IN PONDICHERRY,BUL...
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Android security
The document summarizes a research paper that studied detecting malicious Android applications in official and third-party Android app markets. The researchers developed a system called DroidRanger that uses permission-based filtering and behavioral analysis to detect both known and unknown malware. DroidRanger revealed 211 malicious apps total, with 32 from the official market and 179 from alternative markets. It also discovered a sophisticated zero-day malware with 40 samples, 11 of which were in the official market.
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San Francisco: As coronavirus fears continued to mount on Thursday, Twitter expanded its ban on “dehumanizing language” to include disease.
“A rule barring such hate speech targeting religious groups now applies to language that dehumanizes on the basis of age, disability, or disease,” the Twitter safety team said in an online post.
“Our primary focus is on addressing the risks of offline harm, and research shows that dehumanizing language increases that risk,” it said.
Twitter will delete tweets already posted that break the rules, relying on users to report offending comments.
Examples of rule-breaking tweets included posts that refer to people with a disease as “rats that contaminate everyone around them” or people with a particular disability as “subhuman.”
Twitter and other social networks have been struggling with removing hateful and abusive content while remaining open platforms.
The online services have also faced accusations that the curbs suppress some political views.
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This document discusses four areas of research related to social tagging applications. The first focuses on using facets to organize tags in social tagging systems. The second examines how library catalogues can function as social spaces through user-generated metadata. The third explores motivations for using social tagging applications. The fourth looks at how social tags can help design catalogue records to meet different cultural needs.
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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.
Live Social Semantics @ ESWC2010
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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.
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.orgTwitter
Twitter is a social media platform launched in 2006 that allows users to post and read short 140-character messages called tweets. It was created by Jack Dorsey, Evan Williams, Biz Stone, and Noah Glass and gained worldwide popularity with over 300 million monthly active users. Users can tweet via the website or mobile apps and content includes tweets, polls, trends, and conversations.
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.
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.
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Fr app e detecting malicious facebook applications
We are the company providing Complete Solution for all Academic Final Year/Semester Student Projects. Our projects are
suitable for B.E (CSE,IT,ECE,EEE), B.Tech (CSE,IT,ECE,EEE),M.Tech (CSE,IT,ECE,EEE) B.sc (IT & CSE), M.sc (IT & CSE),
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Database 5. Screen-shots 6. Video File
SERVICE AT CLOUDTECHNOLOGIES
IEEE, WEB, WINDOWS PROJECTS ON DOT NET, JAVA& ANDROID TECHNOLOGIES,EMBEDDED SYSTEMS,MAT LAB,VLSI DESIGN.
ME, M-TECH PAPER PUBLISHING
COLLEGE TRAINING
Thanks&Regards
cloudtechnologies
# 304, Siri Towers,Behind Prime Hospitals
Maitrivanam, Ameerpet.
Contact:-8121953811,8522991105.040-65511811
cloudtechnologiesprojects@gmail.com
http://cloudstechnologies.in/
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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.
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Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
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Twitter and other social networks have been struggling with removing hateful and abusive content while remaining open platforms.
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Social tagging, facets, and social spaces
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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.
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Website: https://www.jpinfotech.orgTwitter
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Retrieving Hidden Friends: A Collusion PrivacyAttack Against Online Friend Se...
Similar to Spammer Detection and Fake User Identificationon 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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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
Cybercrimes in the Darknet and Their Detections: A Comprehensive Analysis and...
Although the Dark web was originally used for maintaining privacy-sensitive communication for business or intelligence services for defence, government and business organizations, fighting against censorship and blocked content, later, the advantage of technologies behind the Dark web were abused by criminals to conduct crimes which involve drug dealing to the contract of assassinations in a widespread manner. Since the communication remains secure and untraceable, criminals can easily use dark web service via The Onion Router (TOR), can hide their illegal motives and can conceal their criminal activities. This makes it very difficult to monitor and detect cybercrimes over the dark web. With the evolution of machine learning, natural language processing techniques, computational big data applications and hardware, there is a growing interest in exploiting dark web data to monitor and detect criminal activities. Due to the anonymity provided by the Dark Web, the rapid disappearance and the change of the uniform resource locators (URLs) of the resources, it is not as easy to crawl the Drak web and get the data as the usual surface web which limits the researchers and law enforcement agencies to analyse the data. Therefore, there is an urgent need to study the technology behind the Dark web, its widespread abuse, its impact on society and the existing systems, to identify the sources of drug deal or terrorism activities. In this research, we analysed the predominant darker sides of the world wide web (WWW), their volumes, their contents and their ratios. We have performed the analysis of the larger malicious or hidden activities that occupy the major portions of the Dark net; tools and techniques used to identify cybercrimes which happen inside the dark web. We applied a systematic literature review (SLR) approach on the resources where the actual dark net data have been used for research purposes in several areas. From this SLR, we identified the approaches (tools and algorithms) which have been applied to analyse the Dark net data, the key gaps as well as the key contributions of the existing works in the literature. In our study, we find the main challenges to crawl the dark web and collect forum data are: scalability of crawler, content selection trade off, and social obligation for TOR crawler and the limitations of techniques used in automatic sentiment analysis to understand criminals’ forums and thereby monitor the forums. From the comprehensive analysis of existing tools, our study summarizes the most tools. However the forum topics rapidly change as their sources changes; criminals inject noises to obfuscate the forum’s main topic and thus remain undetectable. Therefore supervised techniques fail to address the above challenges. Semi-supervised techniques would be an interesting research direction.
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A Survey of Methods for Spotting Spammers on Twitter
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Python ieee project titles 2021 - 2022 | Machine Learning Final Year Project...
Python ieee project titles 2021 - 2022 | Machine Learning Final Year Project...JAYAPRAKASH JPINFOTECH
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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
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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
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centuries, evolving its structure over time and space. In the present era, these changes have
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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
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Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
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occur natural.
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