EIFDAA: Evaluation of an IDS with function-discarding adversarial attacks in the IIoT. Issue 2 (February 2023)

• Record Type: Journal Article
• Authors: Li, Shiming; Wang, Jingxuan; Wang, Yuhe; Zhou, Guohui; Zhao, Yan
• Is Part Of: Heliyon. Volume 9:Issue 2(2023)
• Publication Date: 2023-02
• Subjects: Adversarial attack -- Intrusion detection system -- Industrial internet of things -- Machine learning; Research -- Periodicals; Medical sciences -- Periodicals; Natural history -- Periodicals; Social sciences -- Periodicals; Earth sciences -- Periodicals; Physical sciences -- Periodicals; 507.2
• DOI: 10.1016/j.heliyon.2023.e13520
• Languages: English
• ISSNs: 2405-8440

Abstract

Abstract: The complexity of the Industrial Internet of Things (IIoT) presents higher requirements for intrusion detection systems (IDSs). An adversarial attack is a threat to the security of machine learning-based IDSs. For such a complex situation, this paper analyses adversarial attackers' ability to deceive IDSs used in the IIoT and proposes the evaluation of an IDS with function-discarding adversarial attacks in the IIoT (EIFDAA), a framework that can evaluate the defence performance of machine learning-based IDSs against various adversarial attack algorithms. This framework is composed of two main processes: adversarial evaluation and adversarial training. Adversarial evaluation can diagnose IDS that is unfitting in adversarial environments. Then, adversarial training is used to treat the weak IDS. In this framework, five well-known adversarial attacks, the fast-gradient sign method (FGSM), basic iterative method (BIM), projected gradient descent (PGD), DeepFool and Wasserstein generative adversarial network with gradient penalty (WGAN-GP) are used to convert attack samples into adversarial samples to simulate the adversarial environment. This study evaluates the capability of mainstream machine learning techniques as intrusion detection models to defend against adversarial attacks, and retrains these detectors to improve the robustness of IDSs through adversarial training. In addition, the framework includes an adversarial attack model that discards the attack function of the attack samples in the IIoT. Through the experimental results on the X-IIoTID dataset, the dropped adversarial detection rate of these detectors to nearly zero demonstrates that an adversarial attack has black-box attack capabilities for these IDSs. Additionally, the improved IDSs retrained with adversarial samples can effectively defend against adversarial attackers while maintaining the original detection rate for the attack samples. EIFDAA is expected to be a solution that can be applied to IDS for improving the robustness in the IIoT.