mlsanityData to 14 Oct 2026

A Comparison of Adversarial Learning Techniques for Malware Detection

arXiv 2308.09958 ↗First posted 19 Aug 2023cs.CR cs.LGcited by 5

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Abstract (arXiv)

Machine learning has proven to be a useful tool for automated malware detection, but machine learning models have also been shown to be vulnerable to adversarial attacks. This article addresses the problem of generating adversarial malware samples, specifically malicious Windows Portable Executable files. We summarize and compare work that has focused on adversarial machine learning for malware detection. We use gradient-based, evolutionary algorithm-based, and reinforcement-based methods to generate adversarial samples, and then test the generated samples against selected antivirus products. We compare the selected methods in terms of accuracy and practical applicability. The results show that applying optimized modifications to previously detected malware can lead to incorrect classification of the file as benign. It is also known that generated malware samples can be successfully used against detection models other than those used to generate them and that using combinations of generators can create new samples that evade detection. Experiments show that the Gym-malware generator, which uses a reinforcement learning approach, has the greatest practical potential. This generator achieved an average sample generation time of 5.73 seconds and the highest average evasion rate of 44.11%. Using the Gym-malware generator in combination with itself improved the evasion rate to 58.35%.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 24 of its 75 references that are in the map. First posted on arXiv 19 Aug 2023.

Builds on

24 of 75 references.

References · 24

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  3. 2022Generative Adversarial Networkscited by 5,627
  4. 2015The Limitations of Deep Learning in Adversarial Settingscited by 790
  5. 2017Generative Adversarial Networks: An Overviewcited by 590
  6. 2017Wild Patterns: Ten Years After the Rise of Adversarial Machine Learningcited by 414
  7. 2018EMBER: An Open Dataset for Training Static PE Malware Machine Learning Modelscited by 165
  8. 2017Malware Detection by Eating a Whole EXEcited by 115
  9. 2017Generating Adversarial Malware Examples for Black-Box Attacks Based on GANcited by 75
  10. 2018Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executablescited by 62
  11. 2018Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learningcited by 61
  12. 2020Functionality-preserving Black-box Optimization of Adversarial Windows Malwarecited by 58
12 more
  1. 2018Exploring Adversarial Examples in Malware Detectioncited by 42
  2. 2019Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binariescited by 31
  3. 2018Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examplescited by 20
  4. 2020Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detectioncited by 18
  5. 2021Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Artcited by 18
  6. 2020Arms Race in Adversarial Malware Detection: A Surveycited by 16
  7. 2021A Survey on Adversarial Attacks for Malware Analysiscited by 16
  8. 2020Against All Odds: Winning the Defense Challenge in an Evasion Competition with Diversificationcited by 5
  9. 2021secml-malware: Pentesting Windows Malware Classifiers with Adversarial EXEmples in Pythoncited by 5
  10. 2020MDEA: Malware Detection with Evolutionary Adversarial Learningcited by 4
  11. 2022Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malwarecited by 2
  12. 2023Combining Generators of Adversarial Malware Examples to Increase Evasion Ratecited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2025Cluster Analysis and Concept Drift Detection in Malwarecited by 2
  2. 2026Adversarial Co-Evolution of Malware and Detection Models: A Bilevel Optimization Perspectivecited by 0
  3. 2026Adversarial Malware Generation in Linux ELF Binaries via Semantic-Preserving Transformationscited by 0
  4. 2026Building an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluationcited by 0
  5. 2026EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usabilitycited by 0