Name
Abstract | ||
|---|---|---|
Fake identities and user accounts (also called \"Sybils\") in online communities represent today a treasure for adversaries to spread fake product reviews, malware and spam on social networks, and Astroturf political campaigns. State-of-the-art in the defense mechanisms includes Automated Turing Tests (ATTs such as CAPTCHAs) and graph-based Sybil detectors. Sybil detectors in social networks leverage the assumption that Sybils will find it hard to befriend real users which leads to Sybils being connected to each other forming strongly connected sub graphs that can be detected using graph theory. However, the large majority of Sybils are in fact successful in integrating themselves into real user communities (such as the case in Twitter and Facebook). In this paper, we first study and compare the current detection mechanisms of Sybil accounts. We also explore various types of Twitter Sybil accounts detection features with the objective of building an effective and practical classifier. In order to build and evaluate our classifier, we collect and manually label a dataset of twitter accounts, including human users, bots, and hybrid (i.e., Tweets are posted by both human and bots). We believe this Twitter Sybils corpus will help researchers in conducting sound measurement studies. We also develop a browser plug-in (that we call Twitter Sybils Detector or TSD for short) that utilizes our classifier and warns the user about possible Sybil accounts before accessing them, upon clicking on a Twitter account. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICMLA.2014.81 | ICMLA |
Keywords | Field | DocType |
feature extraction,decision trees,support vector machines,servers | Graph theory,World Wide Web,Social network,Computer security,Computer science,Server,Turing,CAPTCHA,Malware,Strongly connected component,Spamdexing | Conference |
Citations | PageRank | References |
7 | 0.42 | 18 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mansour Alsaleh | 1 | 7 | 0.42 |
| Abdulrahman Alarifi | 2 | 139 | 16.55 |
| AbdulMalik S. Al-Salman | 3 | 141 | 18.35 |
| Mohammed Alfayez | 4 | 7 | 0.42 |
| Abdulmajeed Almuhaysin | 5 | 7 | 0.42 |