  Network Intrusion Detection System (NIDS) is an essential tool in securing
cyberspace from a variety of security risks and unknown cyberattacks. A number
of solutions have been implemented for Machine Learning (ML), and Deep Learning
(DL) based NIDS. However, all these solutions are vulnerable to adversarial
attacks, in which the malicious actor tries to evade or fool the model by
injecting adversarial perturbed examples into the system. The main aim of this
research work is to study powerful adversarial attack algorithms and their
defence method on DL-based NIDS. Fast Gradient Sign Method (FGSM), Jacobian
Saliency Map Attack (JSMA), Projected Gradient Descent (PGD) and Carlini &
Wagner (C&W) are four powerful adversarial attack methods implemented against
the NIDS. As a defence method, Adversarial Training is used to increase the
robustness of the NIDS model. The results are summarized in three phases, i.e.,
1) before the adversarial attack, 2) after the adversarial attack, and 3) after
the adversarial defence. The Canadian Institute for Cybersecurity Intrusion
Detection System 2017 (CICIDS-2017) dataset is used for evaluation purposes
with various performance measurements like f1-score, accuracy etc.
