Evading Machine Learning Malware Detection

@inproceedings{Anderson2017EvadingML,
  title={Evading Machine Learning Malware Detection},
  author={Hyrum S. Anderson},
  year={2017}
}
Machine learning is a popular approach to signatureless malware detection because it can generalize to never-beforeseen malware families and polymorphic strains. This has resulted in its practical use for either primary detection engines or supplementary heuristic detections by anti-malware vendors. Recent work in adversarial machine learning has shown that models are susceptible to gradient-based and other attacks. In this whitepaper, we summarize the various attacks that have been proposed… CONTINUE READING

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