Mar 28, 2019 · A DQEAF framework using reinforcement learning to evade anti-malware engines is presented. DQEAF trains an AI agent through a neural network by ...
Dec 9, 2024 · We present a DQEAF framework using reinforcement learning to evade anti-malware engines. DQEAF trains an AI agent through neural network by ...
Apr 23, 2019 · A DQEAF framework using reinforcement learning to evade anti-malware engines is presented. DQEAF trains an AI agent through a neural network by ...
A DQEAF framework using reinforcement learning to evade anti-malware engines is presented and has a success rate of 75% and has also been evaluated by other ...
Evading Anti-Malware Engines With Deep Reinforcement Learning · Fang, Zhiyang · Wang, Junfeng · Li, Boya · Wu, Siqi · Zhou, Yingjie · Huang, Haiying ...
In this paper, we propose a reinforcement learning-based framework called , which could generate powerful adversarial malware examples to evade the third-party ...
Aug 19, 2024 · PDF | On Dec 1, 2023, Brian Etter and others published Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement ...
The purpose of the tool is to use artificial intelligence to mutate a malware (PE32 only) sample to bypass AI powered classifiers while keeping its ...
A deep reinforcement learning-based system is proposed to control when to halt the emulation of an unknown file and to improve the detection rate of a deep ...
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Dec 4, 2023 · In this paper, we propose the Shapley prior and establish a prior-guidance-based RL framework, namely PSP-Mal, to generate AEs against Portable Executable (PE) ...