Automated generation of models for fast and precise detection of HTTP-based malware

Malicious software and especially botnets are among the most important security threats in the Internet. Thus, the accurate and timely detection of such threats is of great importance. Detecting machines infected with malware by identifying their malicious activities at the network level is an appealing approach, due to the ease of deployment. Nowadays, the most common communication channels used by attackers to control the infected machines are based on the HTTP protocol. To evade detection, HTTP-based malware adapt their behavior to the communication patterns of the benign HTTP clients, such as web browsers. This poses significant challenges to existing detection approaches like signature-based and behavioral-based detection systems. In this paper, we propose BOTHOUND: a novel approach to precisely detect HTTP-based malware at the network level. The key idea is that implementations of the HTTP protocol by different entities have small but perceivable differences. Building on this observation,BOTHOUND automatically generates models for malicious and benign requests and classifies at real time the HTTP traffic of a monitored network. Our evaluation results demonstrate that BOTHOUND outperforms prior work on identifying HTTP-based botnets, being able to detect a large variety of real-world HTTP-based malware, including advanced persistent threats used in targeted attacks, with a very low percentage of classification errors.