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

Authors: Luca Demetrio, Battista Biggio, Fabio Roli
Published: 2022-07-12

Source: https://arxiv.org/abs/2207.05548

PDF: https://arxiv.org/pdf/2207.05548

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マルウェア拡散手段 ポイズニング DFLに対する攻撃手法

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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.