New framework aims to outsmart malware evasion tricks

Attackers have learned how to trick machine learning malware detectors with small but clever code changes, and researchers say they may finally have an answer. In a new paper, academics from Inria and the CISPA Helmholtz Center for Information Security describe a framework that can withstand these kinds of evasion attempts. Their work focuses on adversarial examples in malware detection, where attackers alter software in ways that preserve its function but confuse the model into … More →

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