mlsanityData to 7 Oct 2026

Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach

arXiv 2402.02600 ↗First posted 4 Feb 2024cs.CR cs.AI cs.LGcited by 1

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Abstract (arXiv)

Adversarial Malware Generation (AMG), the generation of adversarial malware variants to strengthen Deep Learning (DL)-based malware detectors has emerged as a crucial tool in the development of proactive cyberdefense. However, the majority of extant works offer subtle perturbations or additions to executable files and do not explore full-file obfuscation. In this study, we show that an open-source encryption tool coupled with a Reinforcement Learning (RL) framework can successfully obfuscate malware to evade state-of-the-art malware detection engines and outperform techniques that use advanced modification methods. Our results show that the proposed method improves the evasion rate from 27%-49% compared to widely-used state-of-the-art reinforcement learning-based methods.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 36 references that are in the map. First posted on arXiv 4 Feb 2024.

Builds on

11 of 36 references.

References · 11

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  2. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  3. 2019On Evaluating Adversarial Robustnesscited by 442
  4. 2017Malware Detection by Eating a Whole EXEcited by 114
  5. 2018Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learningcited by 60
  6. 2020Functionality-preserving Black-box Optimization of Adversarial Windows Malwarecited by 58
  7. 2018Exploring Adversarial Examples in Malware Detectioncited by 41
  8. 2021Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Artcited by 18
  9. 2020Binary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language Modelcited by 10
  10. 2019Defense Methods Against Adversarial Examples for Recurrent Neural Networkscited by 8
  11. 2021Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A Causal Language Model Approachcited by 4

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  1. 2025Adversarial Agents: Black-Box Evasion Attacks with Reinforcement Learningcited by 3