Detecting Malicious HTTP Redirections Using Trees of User Browsing Activity

The web has become a platform that attackers exploit to infect vulnerable hosts, or deceive victims into buying rogue software. To accomplish this, attackers either inject malicious scripts into popular web sites or manipulate content delivered by servers to exploit vulnerabilities in users' browsers. To hide malware distribution servers, attackers employ HTTP redirections, which automatically redirect users' requests through a series of intermediate web sites, before landing on the final distribution site. In this paper, we develop a methodology to identify malicious chains of HTTP redirections. We build per-user chains from passively collected traffic and extract novel statistical features from them, which capture inherent characteristics from malicious redirection cases. Then, we apply a supervised decision tree classifier to identify malicious chains. Using a large ISP dataset, with more than 15K clients, we demonstrate that our methodology is very effective in accurately identifying malicious chains, with recall and precision values over 90% and up to 98%.