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Deep learning is a thriving field currently stuffed with many practicalapplications and active research topics. It allows computers to learn fromexperience and to understand the world in terms of a hierarchy of concepts,with each being defined through its relations to simpler concepts. Relying onthe strong capabilities of deep learning, we propose a convolutional generativeadversarial network-based (Conv-GAN) framework titled MalFox, targetingadversarial malware example generation against third-party black-box malwaredetectors. Motivated by the rival game between malware authors and malwaredetectors, MalFox adopts a confrontational approach to produce perturbationpaths, with each formed by up to three methods (namely Obfusmal, Stealmal, andHollowmal) to generate adversarial malware examples. To demonstrate theeffectiveness of MalFox, we collect a large dataset consisting of both malwareand benignware programs, and investigate the performance of MalFox in terms ofaccuracy, detection rate, and evasive rate of the generated adversarial malwareexamples. Our evaluation indicates that the accuracy can be as high as 99.0%which significantly outperforms the other 12 well-known learning models.Furthermore, the detection rate is dramatically decreased by 56.8% on average,and the average evasive rate is noticeably improved by up to 56.2%.

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