A Deep Reinforcement Learning Framework to Evade Black-Box Machine Learning Based IoT Malware Detectors Using GAN-Generated Influential Features

In the internet of things (IoT) networks, machine learning (ML) is significantly used for malware and adversary detection. Recently, research has shown that adversarial attacks have put ML-based models at risk. This problem is exacerbated in an IoT environment because of the absence of adequate secu...

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主要な著者:	Rahat Maqsood Arif, Muhammad Aslam, Shaha Al-Otaibi, Ana Maria Martinez-Enriquez, Tanzila Saba, Saeed Ali Bahaj, Amjad Rehman
フォーマット:	Artigo
言語:	Inglês
出版事項:	IEEE 2023-01-01
シリーズ:	IEEE Access
主題:	Generative adversarial network portable executable PE malware adversarial attack malware evasion deep reinforcement learning technological development
オンライン･アクセス:	https://ieeexplore.ieee.org/document/10322868/
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