We explore how formal methods and tools of the verification trade could be used for malware detection and analysis. In particular, we propose a new approach to learning and generalizing from observed malware behaviors based on tree automata inference. Our approach infers k -testable tree automata from system call dataflow dependency graphs. We show how inferred automata can be used for malware recognition and classification.
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