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This paper proposes a generic classification system designed to detectsecurity threats based on the behavior of malware samples. The system relies onstatistical features computed from proxy log fields to train detectors using adatabase of malware samples. The behavior detectors serve as basic reusablebuilding blocks of the multi-level detection architecture. The detectorsidentify malicious communication exploiting encrypted URL strings and domainsgenerated by a Domain Generation Algorithm (DGA) which are frequently used inCommand and Control (C&C), phishing, and click fraud. Surprisingly, veryprecise detectors can be built given only a limited amount of informationextracted from a single proxy log. This way, the computational requirements ofthe detectors are kept low which allows for deployment on a wide range ofsecurity devices and without depending on traffic context such as DNS logs,Whois records, webpage content, etc. Results on several weeks of live trafficfrom 100+ companies having 350k+ hosts show correct detection with a precisionexceeding 95% of malicious flows, 95% of malicious URLs and 90% of infectedhosts. In addition, a comparison with a signature and rule-based solution showsthat our system is able to detect significant amount of new threats.

Learning detectors of malicious web requests for intrusion detection in network traffic