PoisonAmplifier: A Guided Approach of Discovering Compromised Websites through Reversing Search Poisoning Attacks
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Through injecting dynamic script codes into compromised websites, attackers have widely launched search poisoning attacks to achieve their malicious goals, such as spreading spam or scams, distributing malware and launching drive-by download attacks. While most current related work focuses on measuring or detecting specific search poisoning attacks in the crawled dataset, it is also meaningful to design an effective approach to find more compromised websites on the Internet that have been utilized by attackers to launch search poisoning attacks, because those compromised websites essentially become an important component in the search poisoning attack chain. In this paper, we present an active and efficient approach, named PoisonAmplifier, to find compromised websites through tracking down search poisoning attacks. Particularly, starting from a small seed set of known compromised websites that are utilized to launch search poisoning attacks, PoisonAmplifier can recursively find more compromised websites by analyzing poisoned webpages' special terms and links, and exploring compromised web sites' vulnerabilities. Through our 1 month evaluation, PoisonAmplifier can quickly collect around 75K unique compromised websites by starting from 252 verified compromised websites within first 7 days and continue to find 827 new compromised websites on a daily basis thereafter. 2012 Springer-Verlag.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
author list (cited authors)
Zhang, J., Yang, C., Xu, Z., & Gu, G.
complete list of authors
Zhang, Jialong||Yang, Chao||Xu, Zhaoyan||Gu, Guofei