  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 capabilities of deep learning, we propose a convolutional generative
adversarial network-based (Conv-GAN) framework titled MalFox, targeting
adversarial malware example generation against third-party black-box malware
detectors. Motivated by the rival game between malware authors and malware
detectors, MalFox adopts a confrontational approach to produce perturbation
paths, with each formed by up to three methods (namely Obfusmal, Stealmal, and
Hollowmal) to generate adversarial malware examples. To demonstrate the
effectiveness of MalFox, we collect a large dataset consisting of both malware
and benignware programs, 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 99.0%
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%.
