BotGAD: Detecting botnets by capturing group activities in network traffic

Recent malicious attempts are intended to obtain financial benefits using a botnet which has become one of the major Internet security problems. Botnets can cause severe Internet threats such as DDoS attacks, identity theft, spamming, click fraud. In this paper, we define a group activity as an inherent property of the botnet. Based on the group activity model and metric, we develop a botnet detection mechanism, called BotGAD (Botnet Group Activity Detector). BotGAD enables to detect unknown botnets from large scale networks in real-time. Botnets frequently use DNS to rally infected hosts, launch attacks and update their codes. We implemented BotGAD using DNS traffic and showed the effectiveness by experiments on real-life network traces. BotGAD captured 20 unknown and 10 known botnets from two day campus network traces.