Peer-to-peer (P2P) botnets have become one of the major threats in network security for serving as the infrastructure that responsible for various of cyber-crimes. Though a few existing work claimed to detect traditional botnets effectively, the problem of detecting P2P botnets involves more challenges. In this paper, we present PeerHunter, a community behavior analysis based method, which is capable of detecting botnets that communicate via a P2P structure. PeerHunter starts from a P2P hosts detection component. Then, it uses mutual contacts as the main feature to cluster bots into communities. Finally, it uses community behavior analysis to detect potential botnet communities and further identify bot candidates. Through extensive experiments with real and simulated network traces, PeerHunter can achieve very high detection rate and low false positives.
Group or community behavior based methods @cite_6 @cite_16 consider the behavior patterns of a group of bots within the same P2P botnet community. For instance, @cite_26 developed a P2P botnets detection approach that start from building a mutual contact graph of the whole network, then attempt to utilize seeds'' (known bots) to identify the rest of bots within the same botnet. However, most of the time, it is hard to have a seed'' in advance. @cite_6 proposed a group-level behavior analysis based P2P botnets detection method. However, they only considered to use statistical traffic features to cluster P2P hosts, which is subject to P2P botnets that have dynamic or randomized traffic patterns. Besides, their method cannot cope with unknown P2P botnets, which is the common case in botnet detection @cite_2 , because of relying on supervised classification methods (e.g. SVM).
@cite_16 @cite_2 @cite_6 @cite_26
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The decentralized nature of Peer-to-Peer (P2P) botnets makes them difficult to detect. Their distributed nature also exhibits resilience against take-down attempts. Moreover, smarter bots are stealthy in their communication patterns, and elude the standard discovery techniques which look for anomalous network or communication behavior. In this paper, we propose Peer Shark, a novel methodology to detect P2P botnet traffic and differentiate it from benign P2P traffic in a network. Instead of the traditional 5-tuple 'flow-based' detection approach, we use a 2-tuple 'conversation-based' approach which is port-oblivious, protocol-oblivious and does not require Deep Packet Inspection. Peer Shark could also classify different P2P applications with an accuracy of more than 95 . Advanced botnets adopt a peer-to-peer (P2P) infrastructure for more resilient command and control (C&C). Traditional detection techniques become less effective in identifying bots that communicate via a P2P structure. In this paper, we present PeerClean, a novel system that detects P2P botnets in real time using only high-level features extracted from C&C network flow traffic. PeerClean reliably distinguishes P2P bot-infected hosts from legitimate P2P hosts by jointly considering flow-level traffic statistics and network connection patterns. Instead of working on individual connections or hosts, PeerClean clusters hosts with similar flow traffic statistics into groups. It then extracts the collective and dynamic connection patterns of each group by leveraging a novel dynamic group behavior analysis. Comparing with the individual host-level connection patterns, the collective group patterns are more robust and differentiable. Multi-class classification models are then used to identify different types of bots based on the established patterns. To increase the detection probability, we further propose to train the model with average group behavior, but to explore the extreme group behavior for the detection. We evaluate PeerClean on real-world flow records from a campus network. Our evaluation shows that PeerClean is able to achieve high detection rates with few false positives. In this work we show that once a single peer-to-peer (P2P) bot is detected in a network, it may be possible to efficiently identify other members of the same botnet in the same network even before they exhibit any overtly malicious behavior. Detection is based on an analysis of connections made by the hosts in the network. It turns out that if bots select their peers randomly and independently (i.e. unstructured topology), any given pair of P2P bots in a network communicate with at least one mutual peer outside the network with a surprisingly high probability. This, along with the low probability of any other host communicating with this mutual peer, allows us to link local nodes within a P2P botnet together. We propose a simple method to identify potential members of an unstructured P2P botnet in a network starting from a known peer. We formulate the problem as a graph problem and mathematically analyze a solution using an iterative algorithm. The proposed scheme is simple and requires only flow records captured at network borders. We analyze the efficacy of the proposed scheme using real botnet data, including data obtained from both observing and crawling the Nugache botnet.