Abstract: Peer-to-peer (P2P) botnets outperform the traditional Internet relay chat (IRC) botnets in evading detection and they have become a prevailing type of threat to the Internet nowadays. Current methods for detecting P2P botnets, such as similarity analysis of network behavior and machine-learning based classification, cannot handle the challenges brought about by different network scenarios and botnet variants. We noticed that one important but neglected characteristic of P2P bots is that they periodically send requests to update their peer lists or receive commands from botmasters in the command-and-control (C&C) phase. In this paper, we propose a novel detection model named detection by mining regional periodicity (DMRP), including capturing the event time series, mining the hidden periodicity of host behaviors, and evaluating the mined periodic patterns to identify P2P bot traffic. As our detection model is built based on the basic properties of P2P protocols, it is difficult for P2P bots to avoid being detected as long as P2P protocols are employed in their C&C. For hidden periodicity mining, we introduce the so-called regional periodic pattern mining in a time series and present our algorithms to solve the mining problem. The experimental evaluation on public datasets demonstrates that the algorithms are promising for efficient P2P bot detection in the C&C phase.

Key words: P2P botnet detection, Regional periodicity, Apriori, Autocorrelation function, Evaluation function

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