MalFox: Camouflaged Adversarial Malware Example Generation Based on C-GANs Against Black-Box Detectors release_en3iqobwmncnvbmiz2hvt6p564

Abstract

Deep learning is a thriving field currently stuffed with many practical applications and active research topics. It allows computers to learn from experience and to understand the world in terms of a hierarchy of concepts, with each being defined through its relations to simpler concepts. Relying on the strong learning capabilities of deep learning, we propose a convolutional generative adversarial network-based (C-GAN) framework titled MalFox, targeting adversarial malware example generation against third-party black-box detectors. MalFox adopts a novel approach to confrontationally produce perturbation paths, with each formed by up to three methods (namely Obfusmal, Stealmal, and Hollowmal) to generate adversarial malware examples via changing the process of program execution in our implementation. To demonstrate the effectiveness of MalFox, we collect a large dataset consisting of both malware and benignware, and investigate the performance of MalFox in terms of accuracy, detection rate, and evasive rate of the generated adversarial malware examples. Our evaluation indicates that the accuracy can be as high as 99.01% which significantly outperforms the other 6 well-known learning models. Furthermore, the detection rate is dramatically decreased by 44.3% on average, and the average evasive rate is noticeably improved by up to 55.3%.
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