Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware
arXiv:2207.05548v18 citationsh-index: 75
Originality Synthesis-oriented
AI Analysis
This work addresses security vulnerabilities in machine learning systems for malware detection, but it is incremental as it applies known adversarial attack concepts to a specific domain.
The authors tackled the lack of systematic security evaluation tools for machine learning by developing practical attacks, demonstrating this with a case study on Windows malware detection.
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.