Predicting web spam with HTTP session information

Web spam is a widely-recognized threat to the quality and security of the Web. Web spam pages pollute search engine indexes, burden Web crawlers and Web mining services, and expose users to dangerous Web-borne malware. To defend against Web spam, most previous research analyzes the contents of Web pages and the link structure of the Web graph. Unfortunately, these heavyweight approaches require full downloads of both legitimate and spam pages to be effective, making real-time deployment of these techniques infeasible for Web browsers, high-performance Web crawlers, and real-time Web applications. In this paper, we present a lightweight, predictive approach to Web spam classification that relies exclusively on HTTP session information (i.e., hosting IP addresses and HTTP session headers). Concretely, we built an HTTP session classifier based on our predictive technique, and by incorporating this classifier into HTTP retrieval operations, we are able to detect Web spam pages before the ...

• Type: Conference
• Year: 2008
• Where: CIKM
• Authors: Steve Webb, James Caverlee, Calton Pu
• Keywords: CIKM 2008, HTTP Session, Information Management, Web Spam, Web Spam Page