SmartNotes: Application of crowdsourcing to the detection of web threats

We describe a crowdsourcing system, called SmartNotes, which detects security threats related to web browsing, such as Internet scams, deceptive sales of substandard products, and websites with intentionally misleading information. It combines automatically collected data about websites with user votes and comments, and uses them to identify potential threats. We have implemented it as a browser extension, which is available for free public use.

[1]  Eugene Fink,et al.  Learning of personalized security settings , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

[2]  Gordon V. Cormack,et al.  TREC 2006 Spam Track Overview , 2006, TREC.

[3]  Charles L. A. Clarke,et al.  Efficient and effective spam filtering and re-ranking for large web datasets , 2010, Information Retrieval.

[4]  Luca Becchetti,et al.  Link analysis for Web spam detection , 2008, TWEB.

[5]  Lorrie Faith Cranor,et al.  School of phish: a real-world evaluation of anti-phishing training , 2009, SOUPS.

[6]  Stefan Savage,et al.  Spamscatter: Characterizing Internet Scam Hosting Infrastructure , 2007, USENIX Security Symposium.

[7]  Christopher Krügel,et al.  Detection and analysis of drive-by-download attacks and malicious JavaScript code , 2010, WWW '10.

[8]  Collin Jackson,et al.  Robust defenses for cross-site request forgery , 2008, CCS.

[9]  Hector Garcia-Molina,et al.  Web Spam Taxonomy , 2005, AIRWeb.

[10]  Eugene Fink,et al.  Detection of Internet scam using logistic regression , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.

[11]  P. Vittoz.  Man , 1962, Bloom.

[12]  Justin Samuel,et al.  RequestPolicy: Increasing Web Browsing Privacy through Control of Cross-Site Requests , 2009, Privacy Enhancing Technologies.