  The surge in the internet of things (IoT) devices seriously threatens the
current IoT security landscape, which requires a robust network intrusion
detection system (NIDS). Despite superior detection accuracy, existing machine
learning or deep learning based NIDS are vulnerable to adversarial examples.
Recently, generative adversarial networks (GANs) have become a prevailing
method in adversarial examples crafting. However, the nature of discrete
network traffic at the packet level makes it hard for GAN to craft adversarial
traffic as GAN is efficient in generating continuous data like image synthesis.
Unlike previous methods that convert discrete network traffic into a grayscale
image, this paper gains inspiration from SeqGAN in sequence generation with
policy gradient. Based on the structure of SeqGAN, we propose Attack-GAN to
generate adversarial network traffic at packet level that complies with domain
constraints. Specifically, the adversarial packet generation is formulated into
a sequential decision making process. In this case, each byte in a packet is
regarded as a token in a sequence. The objective of the generator is to select
a token to maximize its expected end reward. To bypass the detection of NIDS,
the generated network traffic and benign traffic are classified by a black-box
NIDS. The prediction results returned by the NIDS are fed into the
discriminator to guide the update of the generator. We generate malicious
adversarial traffic based on a real public available dataset with attack
functionality unchanged. The experimental results validate that the generated
adversarial samples are able to deceive many existing black-box NIDS.
