USENIX Security Symposium · 2013 · 86 citations · 19 references
ComputingComputer ScienceComputer SystemsComputer NetworksNetwork SecuritySecurity ProtocolsIncident DetectionIntrusion DetectionNetwork ProtocolsNetwork Traffic AnalysisSoftware-defined NetworkingAdaptive Intrusion DetectionDeploymentNetwork Intrusion DetectionNetwork Traffic Anomaly DetectionNetwork Traffic CaptureNetwork-based Intrusion Detection
In this paper, we present ExecScent, a novel system that aims to mine new, previously unknown C&C domain names from live enterprise network traffic. ExecScent automatically learns control protocol templates (CPTs) from examples of known C&C communications. These CPTs are then adapted to the background of the network where the templates are to be deployed. The goal is to generate hybrid templates that can self-tune to each specific deployment scenario, thus yielding a better trade-off between true and false positives for a given network environment. To the best of our knowledge, ExecScent is the first system to use this type of adaptive C&C traffic models. We implemented a prototype version of ExecScent, and deployed it in three different large networks for a period of two weeks. During the deployment, we discovered many new, previously unknown C&C domains and hundreds of new infected machines, compared to using a large up-to-date commercial C&C domain blacklist. Furthermore, we deployed the new C&C domains mined by ExecScent to six large ISP networks, discovering more than 25,000 new infected machines.
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Chih-Chung Chang, Chih‐Jen Lin · ACM Transactions on Intelligent Systems and Technology · 2011 · 41.3K citations
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Yajin Zhou, Xuxian Jiang · 2012 · 2.1K citations