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The authors are witnessing an increasing complexity in the malware analysis scenario. The usage of polymorphic techniques generates a new challenge: it is often difficult to discern the instance of a known polymorphic malware from that of a newly encountered malware family, and to evaluate the impact of patching and code sharing among malware writers in order to prioritize analysis efforts. This paper offers an empirical study on the value of exploiting the complementarity of different information sources in studying malware relationships. By leveraging real-world data generated by a distributed honeypot deployment, they combine clustering techniques based on static and behavioral characteristics of the samples, and they show how this combination helps in detecting clustering anomalies.
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