Summary
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Automating URL Blacklist Generation with Similarity Search Approach Bo SUN Mitsuaki AKIYAMA Takeshi YAGI Mitsuhiro HATADA Tatsuya MORI Publication IEICE TRANSACTIONS on Information and Systems Vol.E99-D No.4 pp.873-882 Publication Date: 2016/04/01 Publicized: 2016/01/13 Online ISSN: 1745-1361 DOI: 10.1587/transinf.2015ICP0027 Type of Manuscript: Special Section PAPER (Special Section on Information and Communication System Security) Category: Web security Keyword: drive-by-download, URL blacklist, search space, machine learning, web client honeypot, Full Text: PDF(2MB)>>
Summary: Modern web users may encounter a browser security threat called drive-by-download attacks when surfing on the Internet. Drive-by-download attacks make use of exploit codes to take control of user's web browser. Many web users do not take such underlying threats into account while clicking URLs. URL Blacklist is one of the practical approaches to thwarting browser-targeted attacks. However, URL Blacklist cannot cope with previously unseen malicious URLs. Therefore, to make a URL blacklist effective, it is crucial to keep the URLs updated. Given these observations, we propose a framework called automatic blacklist generator (AutoBLG) that automates the collection of new malicious URLs by starting from a given existing URL blacklist. The primary mechanism of AutoBLG is expanding the search space of web pages while reducing the amount of URLs to be analyzed by applying several pre-filters such as similarity search to accelerate the process of generating blacklists. AutoBLG consists of three primary components: URL expansion, URL filtration, and URL verification. Through extensive analysis using a high-performance web client honeypot, we demonstrate that AutoBLG can successfully discover new and previously unknown drive-by-download URLs from the vast web space. | ||||||||||
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