On and off the manifold: Generation and detection of adversarial attacks in IIoT networks

Document Type

Journal Article

Publication Title

Journal of Network and Computer Applications

Volume

235

Publisher

Elsevier

School

Centre for Artificial Intelligence and Machine Learning (CAIML) / School of Science

RAS ID

77425

Comments

Al-Fawa’reh, M., Abu-Khalaf, J., Janjua, N., & Szewczyk, P. (2025). On and off the manifold: Generation and detection of adversarial attacks in IIoT networks. Journal of Network and Computer Applications, 235. https://doi.org/10.1016/j.jnca.2024.104102

Abstract

Network Intrusion Detection Systems (NIDS), which play a crucial role in defending Industrial Internet of Things (IIoT) networks, often utilize Deep Neural Networks (DNN) for their pattern recognition capabilities. However, these systems remain susceptible to sophisticated adversarial attacks, particularly on-manifold and off-manifold attacks, which skillfully evade detection. This paper addresses the limitations in existing research, focusing primarily on: the predominant focus on off-manifold attacks, while often overlooking subtler yet potent on-manifold attacks; a lack of consideration for the functional behavior of these attacks; reliance on detailed knowledge of the target NIDS for creating attacks; and the need for detailed knowledge about the creation process of adversarial attacks for effective detection. This paper introduces the Saliency Adversarial Autoencoder (SAAE), designed for generating on-manifold attacks through latent space perturbations. This dual-space perturbation approach enables SAAE to efficiently create stealthy attacks that blend with normal network behavior, posing significant challenges to state-of-the-art (SOTA) NIDS. To counter these advanced threats, we propose an attack-agnostic defence mechanism utilizing a fusion-based Autoencoder (AE) with disentangled representations. This defence is adept at detecting threats within the manifold, significantly enhancing NIDS robustness. Comparative assessments with SOTA DNN and Deep Reinforcement Learning (DRL) models highlight the effectiveness of our approach. The SAAE model markedly reduces True Positive Rates (TPR) in these systems. For DNNs, TPR dropped from 99.72% to 41.5%, and for DRLs, from 95.6% to 63.94%. Conversely, our defence model shows high TPR in detecting these attacks, registering 94% for DNNs and 92% for DRLs. Additionally, we release our dataset, named OOM-X-IIoTID1, which includes On/Off manifold adversarial attacks, a first in the field, to facilitate further research and development in cybersecurity.

DOI

10.1016/j.jnca.2024.104102

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