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.

翻译：网络入侵检测系统是保障网络空间免受多种安全风险和未知网络攻击的重要工具。已有多种基于机器学习和深度学习的网络入侵检测解决方案得以实现。然而，所有这些解决方案都容易受到对抗攻击的影响，其中恶意行为者通过向系统注入对抗性扰动样本来试图规避或欺骗模型。本研究的主要目的是研究针对深度学习网络入侵检测系统的强大对抗攻击算法及其防御方法。快速梯度符号方法、雅可比显著图攻击、投影梯度下降和Carlini & Wagner攻击是四种针对网络入侵检测系统实施的强大对抗攻击方法。作为防御方法，采用对抗训练来增强网络入侵检测模型的鲁棒性。结果分三个阶段总结：1）对抗攻击前，2）对抗攻击后，以及3）对抗防御后。使用加拿大网络安全研究所入侵检测系统2017数据集进行评估，并采用各种性能指标如F1分数和准确率等。