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In this article, we propose Segugio , a novel defense system that allows for efficiently tracking the occurrence of new malware-control domain names in very large ISP networks. Segugio passively monitors the DNS traffic to build a machine-domain bipartite graph representing who is querying what . After labeling nodes in this query behavior graph that are known to be either benign or malware-related, we propose a novel approach to accurately detect previously unknown malware-control domains. We implemented a proof-of-concept version of Segugio and deployed it in large ISP networks that serve millions of users. Our experimental results show that Segugio can track the occurrence of new malware-control domains with up to 94% true positives (TPs) at less than 0.1% false positives (FPs). In addition, we provide the following results: (1) we show that Segugio can also detect control domains related to new, previously unseen malware families, with 85% TPs at 0.1% FPs; (2) Segugio’s detection models learned on traffic from a given ISP network can be deployed into a different ISP network and still achieve very high detection accuracy; (3) new malware-control domains can be detected days or even weeks before they appear in a large commercial domain-name blacklist; (4) Segugio can be used to detect previously unknown malware-infected machines in ISP networks; and (5) we show that Segugio clearly outperforms domain-reputation systems based on Belief Propagation.

Abstract. In this paper we present PeerRush, a novel system for the identification of unwanted P2P traffic. Unlike most previous work, PeerRush goes beyond P2P traffic detection, and can accurately categorize the detected P2P traffic and attribute it to specific P2P applications, including malicious applications such as P2P botnets. PeerRush achieves these results without the need of deep packet inspection, and can accurately identify applications that use encrypted P2P traffic. We implemented a prototype version of PeerRush and performed an extensive evaluation of the system over a variety of P2P traffic datasets. Our results show that we can detect all the considered types of P2P traffic with up to 99.5% true positives and 0.1% false positives. Furthermore, PeerRush can attribute the P2P traffic to a specific P2P application with a misclassification rate of 0.68% or less.

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