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Malware Analysis with Tree Automata Inference
If you have a question about this talk, please contact Jonathan Hayman.
The underground malware-based economy is flourishing and it is evident that the classical ad-hoc signature detection methods are becoming insufficient. Malware authors seem to share some source code and malware samples often feature similar behaviors, but such commonalities are difficult to detect with signature-based methods because of an increasing use of numerous freely-available randomized obfuscation tools. To address this problem, the security community is actively researching behavioral detection methods that commonly attempt to understand and differentiate how malware behaves, as opposed to just detecting syntactic patterns. Continuing that line of research, in this talk I will explore how grammatical inference and tools of the verification trade could be used for malware detection and analysis. I will present a new approach to learning and generalizing from observed malware behaviors based on tree automata inference. In particular, I will show how one can infer k-testable tree automata from system call dataflow dependency graphs and discuss the use of inferred automata in malware recognition and classification. At the end, I will briefly survey some other related work I have done in recent past, as well as hint the future research directions.
This talk is part of the Logic and Semantics Seminar (Computer Laboratory) series.
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Other talksPublic Policy Seminar: A Strategy for the UK Steel Industry Welcome and Introduction Masterclass: The health impacts of volcanic gases More Diversity = Better Science: International Women’s Day 2017 Dr Amir Horowitz: Harnessing NK cell functions for treatments against viruses and cancers A couple of efficient prediction models for underground railway-induced vibrations