A lightweight optimized intrusion detection system using machine learning for edge-based IIoT security

The Industrial Internet of Things (IIoT) attributes to intelligent sensors and actuators for better manufacturing and industrial operations. At the same time, IIoT devices must be secured from the potentially catastrophic effects of eventual attacks, and this necessitates real-time prediction and preventive strategies for cyber-attack vectors. Due to this, the objective of this investigation is to obtain a high-accuracy intrusion detection technique with a minimum payload. As the experimental process, we have utilized the IIoT network security dataset, namely WUSTL-IIOT-2021. The feature selection technique Particle Swarm Optimization (PSO) and feature reduction techniques such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) are applied. Additionally, the Generalized Additive Model (GAM) and Multivariate Adaptive Regression Splines (MARS) are used to detect payloads that can interfere with the normal operation of an application. Both PSO and PCA combined with MARS have produced predictive results with an exceptional accuracy of 100%. Yet, the trained Machine Learning (ML) model is quantized with 4-bit and 8-bit, and it is deployed on Azure IoT Edge to simulate edge devices. Experimental results show that the latency of the model was reduced by 25% on quantization.