Malware silently infects millions of systems every year through drive-by downloads, i.e., client-side exploits against web browsers or browser helper objects that are triggered when unsuspecting users visit a page containing malicious content. Identifying and blacklisting websites that distribute malicious content or redirect to a distributing page is an important part of our defense strategy against such attacks. However, building such lists is fraught with challenges of scale, timeliness and deception due to evasive strategies employed by adversaries. In this work, we describe alice@home, a distributed approach to overcoming these challenges and actively identifying malware distribution sites. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Erete, I., Yegneswaran, V., & Porras, P. (2009). ALICE@home: Distributed framework for detecting malicious sites. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5758 LNCS, pp. 362–364). https://doi.org/10.1007/978-3-642-04342-0_25
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