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Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware

Cryptography and Security 2022-07-13 v1 Machine Learning
Authors: Luca Demetrio , Battista Biggio , Fabio Roli
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Abstract

While machine learning is vulnerable to adversarial examples, it still lacks systematic procedures and tools for evaluating its security in different application contexts. In this article, we discuss how to develop automated and scalable security evaluations of machine learning using practical attacks, reporting a use case on Windows malware detection.

Keywords

malware detection privacy attacks on machine learning adversarial examples

Cite

@article{arxiv.2207.05548,
  title  = {Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware},
  author = {Luca Demetrio and Battista Biggio and Fabio Roli},
  journal= {arXiv preprint arXiv:2207.05548},
  year   = {2022}
}

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