Explore how MEME, a cutting-edge algorithm combining malware evasion and model extraction, outperforms state-of-the-art methods in generating evasive malware.
Authors: Maria Rigaki, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic and maria.rigaki@fel.cvut.cz; Sebastian Garcia, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic and sebastian.garcia@agents.fel.cvut.cz.
is to learn a reinforcement learning policy that selects the appropriate modifications to a malicious Windows binary file in order for it to evade a target detection model while using a limited amount of interactions with the target. The core idea is to use observations and labels collected during the interaction with the reinforcement learning environment and use them with an auxiliary dataset to train a surrogate model of the target.
learns a policy that evades the targets with an evasion rate of 32-73%. outperforms the state-of-the-art methods in terms of evasion capabilities in almost all cases, producing evasive malware with an evasion rate in the range of 32-73%. It also produces surrogate models with a prediction label agreement with the respective target models between 97-99%. The surrogate could be used to fine-tune and improve the evasion rate in the future.
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