Adversarial Robust and Explainable Network Intrusion Detection Systems Based on Deep Learning

Abstract

The ever-evolving cybersecurity environment has given rise to sophisticated adversaries who constantly explore new ways to attack cyberinfrastructure. Recently, the use of deep learning-based intrusion detection systems has been on the rise. This rise is due to deep neural networks (DNN) complexity and efficiency in making anomaly detection activities more accurate. However, the complexity of these models makes them black-box models, as they lack explainability and interpretability. Not only is the DNN perceived as a black-box model, but recent research evidence has also shown that they are vulnerable to adversarial attacks. This paper developed an adversarial robust and explainable network intrusion detection system based on deep learning by applying adversarial training and implementing explainable AI techniques. In our experiments with the NSL-KDD dataset, the PGD adversarial-trained model was a more robust model than DeepFool adversarial-trained and FGSM adversarial-trained models, with a ROC-AUC of 0.87. The FGSM attack did not affect the PGD adversarial-trained model’s ROC-AUC, while the DeepFool attack caused a minimal 9.20% reduction in PGD adversarial-trained model’s ROC-AUC. PGD attack caused a 15.12% reduction in the DeepFool adversarial-trained model’s ROC-AUC and a 12.79% reduction in FGSM trained model’s ROC-AUC.

i-2NIDS Novel Intelligent Intrusion Detection Approach for a Strong Network Security

“…In addition, the proposed approach performs well on imbalanced data and has a better ability to identify different cyberattacks as it has been mentioned in the previous sub-sections. Therefore, RF-2NIDS can be considered as a robust model against adversarial attacks, whereby the only purpose is to trick the learnt model to make incorrect results (McCarthy et al, 2022;Sauka et al, 2022;Zhao et al, 2022). This will be proven in the next contribution.…”

Statistical Detection of Adversarial Examples in Blockchain-Based Federated Forest In-Vehicle Network Intrusion Detection Systems

“…Debicha, et al [35] investigated the impact of the adversarial attack on deep learning-based IDS and proposed a defence mechanism using adversarial training. Sauka, et al [36] focused on the problem of adversarial vulnerability and explainability of deep learning-based IDS and proposed adversarial training and AI explainable framework based on SHAP. Meanwhile, [37] under study the impact of adversarial attacks on ML and deep-learning based IDS for IoT security domain.…”