  Machine learning has been used to detect new malware in recent years, while
malware authors have strong motivation to attack such algorithms. Malware
authors usually have no access to the detailed structures and parameters of the
machine learning models used by malware detection systems, and therefore they
can only perform black-box attacks. This paper proposes a generative
adversarial network (GAN) based algorithm named MalGAN to generate adversarial
malware examples, which are able to bypass black-box machine learning based
detection models. MalGAN uses a substitute detector to fit the black-box
malware detection system. A generative network is trained to minimize the
generated adversarial examples' malicious probabilities predicted by the
substitute detector. The superiority of MalGAN over traditional gradient based
adversarial example generation algorithms is that MalGAN is able to decrease
the detection rate to nearly zero and make the retraining based defensive
method against adversarial examples hard to work.
