Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 8 of its 19 references that are in the map. First posted on arXiv 12 Jul 2022.
Builds on
8 of 19 references.
References · 8
- 2013Intriguing properties of neural networkscited by 5,114
- 2017Evasion Attacks against Machine Learning at Test Timecited by 686
- 2017Wild Patterns: Ten Years After the Rise of Adversarial Machine Learningcited by 411
- 2018Audio Adversarial Examples: Targeted Attacks on Speech-to-Textcited by 268
- 2020Functionality-preserving Black-box Optimization of Adversarial Windows Malwarecited by 58
- 2017Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detectioncited by 51
- 2019Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytescited by 31
- 2018Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examplescited by 19
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Citing papers per year: 2023: 2.
2 papers.