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http://dx.doi.org/10.7236/IJASC.2021.10.3.163

BM3D and Deep Image Prior based Denoising for the Defense against Adversarial Attacks on Malware Detection Networks  

Sandra, Kumi (Department of Computer Engineering, Dongseo University)
Lee, Suk-Ho (Department of Computer Engineering, Dongseo University)
Publication Information
International journal of advanced smart convergence / v.10, no.3, 2021 , pp. 163-171 More about this Journal
Abstract
Recently, Machine Learning-based visualization approaches have been proposed to combat the problem of malware detection. Unfortunately, these techniques are exposed to Adversarial examples. Adversarial examples are noises which can deceive the deep learning based malware detection network such that the malware becomes unrecognizable. To address the shortcomings of these approaches, we present Block-matching and 3D filtering (BM3D) algorithm and deep image prior based denoising technique to defend against adversarial examples on visualization-based malware detection systems. The BM3D based denoising method eliminates most of the adversarial noise. After that the deep image prior based denoising removes the remaining subtle noise. Experimental results on the MS BIG malware dataset and benign samples show that the proposed denoising based defense recovers the performance of the adversarial attacked CNN model for malware detection to some extent.
Keywords
Malware detection; Deep Learning; Adversarial Examples; Denoising; BM3D; Deep Image Prior;
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