While ML models for malware detection have become an industry standard for heuristically detecting malware, signature-based detection still proliferates thanks to ease of updates, transparency of detection logic, and operability in compute-constrained environments. In this work, we propose an interpretable machine learning model that can generate signatures tuned to optimize detection and minimize false positives on a given corpus of malware and benign samples. On a corpus of malicious and benign ELF executables targeting i386 and amd64, we observe detection rates in the 80% range with a false positive rate of 0% on the benign corpus with a few hundred YARA rules...
By: Mathy Vanhoef
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