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Enhancing Cybersecurity with MEME: Reinforcement Learning for Adversarial Malware Evasionby@memeology
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Enhancing Cybersecurity with MEME: Reinforcement Learning for Adversarial Malware Evasion

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MEME, powered by model-based reinforcement learning, excels in generating adversarial malware that evades antivirus systems and trains precise surrogate models. Its success hints at broader applications in cybersecurity and threat defense, with future research focusing on optimizations like ensemble surrogates.
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Authors:

(1) Maria Rigaki, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic and [email protected];

(2) Sebastian Garcia, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic and [email protected].

Abstract & Introduction

Threat Model

Background and Related Work

Methodology

Experiments Setup

Results

Discussion

Conclusion, Acknowledgments, and References

Appendix

8 Conclusions

By employing model-based reinforcement learning, MEME generates adversarial malware samples that successfully evade antivirus systems and train a surrogate model mimicking the target classifier accurately. Our experiments show that MEME surpasses existing methods in evasion rate, suggesting its potential for various applications, such as testing model robustness and enhancing cyber security against advanced persistent threats. Future work may involve exploring ensemble surrogates and other optimizations to enhance MEME’s performance further.


Acknowledgments

The authors acknowledge support from the Strategic Support for the Development of Security Research in the Czech Republic 2019–2025 (IMPAKT 1) program, by the Ministry of the Interior of the Czech Republic under No. VJ02010020 – AI-Dojo: Multi-agent testbed for the research and testing of AI-driven cyber security technologies. The authors acknowledge the support of NVIDIA Corporation with the donation of a Titan V GPU used for this research.


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