Enhancing Network Intrusion Detection Systems: A Multi-Layer Ensemble Approach to Mitigate Adversarial Attacks

TL;DR

This paper proposes a multi-layer ensemble defense mechanism for machine learning-based Network Intrusion Detection Systems to improve robustness against adversarial attacks generated by GAN and FGSM methods.

Contribution

It introduces a novel multilayer defense combining stacking classifiers and autoencoders, enhanced with adversarial training, to mitigate vulnerabilities in NIDS.

Findings

Increased resilience to adversarial attacks on NIDS.

Effective detection of malicious network traffic using the proposed method.

Improved robustness demonstrated on UNSW-NB15 and NSL-KDD datasets.

Abstract

Adversarial examples can represent a serious threat to machine learning (ML) algorithms. If used to manipulate the behaviour of ML-based Network Intrusion Detection Systems (NIDS), they can jeopardize network security. In this work, we aim to mitigate such risks by increasing the robustness of NIDS towards adversarial attacks. To that end, we explore two adversarial methods for generating malicious network traffic. The first method is based on Generative Adversarial Networks (GAN) and the second one is the Fast Gradient Sign Method (FGSM). The adversarial examples generated by these methods are then used to evaluate a novel multilayer defense mechanism, specifically designed to mitigate the vulnerability of ML-based NIDS. Our solution consists of one layer of stacking classifiers and a second layer based on an autoencoder. If the incoming network data are classified as benign by the first layer, the second layer is activated to ensure that the decision made by the stacking classifier is correct. We also incorporated adversarial training to further improve the robustness of our solution. Experiments on two datasets, namely UNSW-NB15 and NSL-KDD, demonstrate that the proposed approach increases resilience to adversarial attacks.