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With the growth of adversarial attacks against machine learning models,several concerns have emerged about potential vulnerabilities in designing deepneural network-based intrusion detection systems (IDS). In this paper, we studythe resilience of deep learning-based intrusion detection systems againstadversarial attacks. We apply the min-max (or saddle-point) approach to trainintrusion detection systems against adversarial attack samples in NSW-NB 15dataset. We have the max approach for generating adversarial samples thatachieves maximum loss and attack deep neural networks. On the other side, weutilize the existing min approach [2] [9] as a defense strategy to optimizeintrusion detection systems that minimize the loss of the incorporatedadversarial samples during the adversarial training. We study and measure theeffectiveness of the adversarial attack methods as well as the resistance ofthe adversarially trained models against such attacks. We find that theadversarial attack methods that were designed in binary domains can be used incontinuous domains and exhibit different misclassification levels. We finallyshow that principal component analysis (PCA) based feature reduction can boostthe robustness in intrusion detection system (IDS) using a deep neural network(DNN).

Investigating Resistance of Deep Learning-based IDS against Adversaries using min-max Optimization