From Concept to Prototype: Developing and Testing GAAINet for Industrial IoT Intrusion Detection

Intrusion detection is a growing area of concern in Industrial Internet of Things (IIoT) systems. This is largely due to the fact that IIoT systems are typically used to augment the operation of Critical Information Infrastructures, the compromise of which could result in severe consequences for industries or even nations. In addition, IIoT is a relatively new technological development which introduces new vulnerabilities. Machine learning methods are increasingly being applied to IIoT intrusion detection. However, the data imbalance prevalent in IIoT intrusion detection datasets can limit the performance of intrusion detection algorithms due to the significantly smaller amount of attack samples. As such, generative models have been applied to address the data imbalance problem by modelling distributions of intrusion detection datasets in order to generate synthetic attack samples. Current work presents the implementation of a Generative Adversarial Artificial Immune Network (GAAINet) as an approach for addressing data imbalance IIoT intrusion detection. Experimental results show that GAAINet could generate synthetic attack samples for the WUSTL-IIoT-2021 dataset. The resulting balanced dataset was used to train an Artificial Immune Network classifier, which achieved a detection accuracy of 99.13% for binary classification and 98.87% for multi-class classification.