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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Lee, Sangho Kim, Jong |
| Copyright Year | 2004 |
| Abstract | Twitter is prone to malicious tweets containing URLs for spam, phishing, and malware distribution. Conventional Twitter spam detection schemes utilize account features such as the ratio of tweets containing URLs and the account creation date, or relation features in the Twitter graph. These detection schemes are ineffective against feature fabrications or consume much time and resources. Conventional suspicious URL detection schemes utilize several features including lexical features of URLs, URL redirection, HTML content, and dynamic behavior. However, evading techniques such as time-based evasion and crawler evasion exist. In this paper, we propose WarningBird, a suspicious URL detection system for Twitter. Our system investigates correlations of URL redirect chains extracted from several tweets. Because attackers have limited resources and usually reuse them, their URL redirect chains frequently share the same URLs. We develop methods to discover correlated URL redirect chains using the frequently shared URLs and to determine their suspiciousness. We collect numerous tweets from the Twitter public timeline and build a statistical classifier using them. Evaluation results show that our classifier accurately and efficiently detects suspicious URLs. We also present WarningBird as a near real-time system for classifying suspicious URLs in the Twitter stream. |
| Sponsorship | IEEE Computer Society |
| Starting Page | 183 |
| Ending Page | 195 |
| Page Count | 13 |
| File Size | 2114273 |
| File Format | |
| ISSN | 15455971 |
| Volume Number | 10 |
| Issue Number | 3 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-05-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Twitter Feature extraction Crawlers Servers IP networks Browsers Training classification Suspicious URL twitter URL redirection conditional redirection |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Science Electrical and Electronic Engineering |
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