Strider Search Ranger: Towards an Autonomic Anti-Spam Search Engine

Search spammers use questionable search engine optimization techniques to promote their spam links into top search results. Large-scale spammers target commerce queries that they can monetize and attempt to spam as many top search results of those queries as possible. We model the large-scale search spam problem as that of defending against correlated attacks on search rankings across multiple keywords, and propose an autonomic anti-spam approach based on self-monitoring and self- protection. In this new approach, search engines monitor and correlate their own search results of spammer-targeted keywords to detect large-scale spam attacks that have successfully bypassed their current anti-spam solutions. They then initiate self-protection through targeted patrol of spam-heavy domains, targeted hunting at the sources of successful spam, and strengthening of specific weakness in the ranking algorithms. We describe the strider search ranger system which implements this new approach, and focus on its use to defend against an important class of search spam - the redirection spam - as a demonstration of the general concept. We evaluate the system by testing it against actual search results and show that it can detect useful spam patterns and eliminate a significant amount of spam for all three major search engines.