mlsanityData to 14 Oct 2026

EGAN: Evolutional GAN for Ransomware Evasion

arXiv 2405.12266 ↗First posted 20 May 2024cs.CR cs.AI cs.LGcited by 0

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Abstract (arXiv)

Adversarial Training is a proven defense strategy against adversarial malware. However, generating adversarial malware samples for this type of training presents a challenge because the resulting adversarial malware needs to remain evasive and functional. This work proposes an attack framework, EGAN, to address this limitation. EGAN leverages an Evolution Strategy and Generative Adversarial Network to select a sequence of attack actions that can mutate a Ransomware file while preserving its original functionality. We tested this framework on popular AI-powered commercial antivirus systems listed on VirusTotal and demonstrated that our framework is capable of bypassing the majority of these systems. Moreover, we evaluated whether the EGAN attack framework can evade other commercial non-AI antivirus solutions. Our results indicate that the adversarial ransomware generated can increase the probability of evading some of them.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 24 references that are in the map. First posted on arXiv 20 May 2024.

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References · 14

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
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  3. 2017Evolution Strategies as a Scalable Alternative to Reinforcement Learningcited by 574
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  5. 2017Malware Detection by Eating a Whole EXEcited by 115
  6. 2017Generating Adversarial Malware Examples for Black-Box Attacks Based on GANcited by 75
  7. 2018Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learningcited by 61
  8. 2015Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Featurescited by 48
  9. 2018Adversarial Deep Learning for Robust Detection of Binary Encoded Malwarecited by 32
  10. 2020Arms Race in Adversarial Malware Detection: A Surveycited by 16
  11. 2021Black-box adversarial attacks using Evolution Strategiescited by 11
  12. 2022MERLIN -- Malware Evasion with Reinforcement LearnINgcited by 11
2 more
  1. 2020MAB-Malware: A Reinforcement Learning Framework for Attacking Static Malware Classifierscited by 8
  2. 2021EvoBA: An Evolution Strategy as a Strong Baseline forBlack-Box Adversarial Attackscited by 4

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