Unsupervised detection of botnet activities using frequent pattern tree mining

A botnet is a network of remotely-controlled infected computers that can send spam, spread viruses, or stage denial-of-service attacks, without the consent of the computer owners. Since the beginning of the 21st century, botnet activities have steadily increased, becoming one of the major concerns for Internet security. In fact, botnet activities are becoming more and more difficult to be detected, because they make use of Peer-to-Peer protocols (eMule, Torrent, Frostwire, Vuze, Skype and many others). To improve the detectability of botnet activities, this paper introduces the idea of association analysis in the field of data mining, and proposes a system to detect botnets based on the FP-growth (Frequent Pattern Tree) frequent item mining algorithm. The detection system is composed of three parts: packet collection processing, rule mining, and statistical analysis of rules. Its characteristic feature is the rule-based classification of different botnet behaviors in a fast and unsupervised fashion. The effectiveness of the approach is validated in a scenario with 11 Peer-to-Peer host PCs, 42063 Non-Peer-to-Peer host PCs, and 17 host PCs with three different botnet activities (Storm, Waledac and Zeus). The recognition accuracy of the proposed architecture is shown to be above 94%. The proposed method is shown to improve the results reported in literature.