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arXiv · 1906.09084

Joint Detection of Malicious Domains and Infected Clients

Paul Prasse·Rene Knaebel·Lukas Machlica·Tomas Pevny·Tobias Scheffer
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Abstract

Detection of malware-infected computers and detection of malicious web domains based on their encrypted HTTPS traffic are challenging problems, because only addresses, timestamps, and data volumes are observable. The detection problems are coupled, because infected clients tend to interact with malicious domains. Traffic data can be collected at a large scale, and antivirus tools can be used to identify infected clients in retrospect. Domains, by contrast, have to be labeled individually after forensic analysis. We explore transfer learning based on sluice networks; this allows the detection models to bootstrap each other. In a large-scale experimental study, we find that the model outperforms known reference models and detects previously unknown malware, previously unknown malware families, and previously unknown malicious domains.

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Paul Prasse, Rene Knaebel, Lukas Machlica, Tomas Pevny, Tobias Scheffer. 2019-06-21. Joint Detection of Malicious Domains and Infected Clients. https://doi.org/10.1007/s10994-019-05789-z

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