Biblio

Botnets have long been used for malicious purposes with huge economic costs to the society. With the proliferation of cheap but non-secure Internet-of-Things (IoT) devices generating large amounts of data, the potential for damage from botnets has increased manifold. There are several approaches to detect bots or botnets, though many traditional techniques are becoming less effective as botnets with centralized command & control structure are being replaced by peer-to-peer (P2P) botnets which are harder to detect. Several algorithms have been proposed in literature that use graph analysis or machine learning techniques to detect the overlay structure of P2P networks in communication graphs. Many of these algorithms however, depend on the availability of a universal communication graph or a communication graph aggregated from several ISPs, which is not likely to be available in reality. In real world deployments, significant gaps in communication graphs are expected and any solution proposed should be able to work with partial information. In this paper, we analyze the effectiveness of some community detection algorithms in detecting P2P botnets, especially with partial information. We show that the approach can work with only about half of the nodes reporting their communication graphs, with only small increase in detection errors.

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.

Botnet detection represents one of the most crucial prerequisites of successful botnet neutralization. This paper explores how accurate and timely detection can be achieved by using supervised machine learning as the tool of inferring about malicious botnet traffic. In order to do so, the paper introduces a novel flow-based detection system that relies on supervised machine learning for identifying botnet network traffic. For use in the system we consider eight highly regarded machine learning algorithms, indicating the best performing one. Furthermore, the paper evaluates how much traffic needs to be observed per flow in order to capture the patterns of malicious traffic. The proposed system has been tested through the series of experiments using traffic traces originating from two well-known P2P botnets and diverse non-malicious applications. The results of experiments indicate that the system is able to accurately and timely detect botnet traffic using purely flow-based traffic analysis and supervised machine learning. Additionally, the results show that in order to achieve accurate detection traffic flows need to be monitored for only a limited time period and number of packets per flow. This indicates a strong potential of using the proposed approach within a future on-line detection framework.