A Lightweight Hybrid Intrusion Detection Framework using Machine Learning for Edge-Based IIoT Security

Abstract

Due to the development of cloud computing and Internet of Things (IoT) environments, such as healthcare systems, telecommunications and Industry 4.0 or Industrial IoT (IIoT) many daily services are transformed. Therefore, Security issues become useful to better protect these novel technologies. IIoT security represents a real challenge for industry actors and academic research. A set of security approaches, such as intrusion detection are integrated to improve IIoT environments security. Hence, an Intrusion Detection System (IDS) aims to monitor, detect an intrusion in real time and then make reliable decisions. Many recent IDS incorporate Machine Learning (ML) techniques to improve their Accuracy (ACC), precision and Detection Rate (DR). This paper presents a hybrid IDS for Edge-Based IIoT Security using ML techniques. This new hybrid framework is based on misuse and anomaly detection using K-Nearest Neighbor (K-NN) and Principal Component Analysis (PCA) techniques. Specifically, the K-NN classifier has been incorporated to improve detection accuracy and make effective decision and the PCA is used for an enhanced feature engineering and training process. The obtained results have proven that our proposed Framework presents many advantages compared with other recent models. It gives good results with 99.10% ACC, 98.4% DR 2.7% False Alarm Rate (FAR) on NSL-KDD dataset and 98.2% ACC, 97.6% DR, 2.9% FAR on Bot-IoT dataset.