Deep learning-based intrusion detection approach for securing industrial Internet of Things

Abstract

The widespread deployment of the Internet of Things (IoT) into critical sectors such as industrial and manufacturing has resulted in the Industrial Internet of Things (IIoT). The IIoT consists of sensors, actuators, and smart devices that communicate with one another to optimize manufacturing and industrial processes. Although IIoT provides various benefits to both service providers and consumers, security and privacy remain a big challenge. An intrusion detection system (IDS) has been utilized to mitigate cyberattacks in such a connected network. However, many existing solutions for IDS in IIoT suffer from the lack of comprehensiveness of the types of attack the network is exposed to, high feature dimension, models built on out-of-date datasets, and a lack of focus on the problem of imbalanced datasets. To address the aforementioned issues, we propose an intelligent detection system for identifying cyberattacks in Industrial IoT networks. The proposed model uses the singular value decomposition (SVD) technique to reduce data features and improve detection results. We use the synthetic minority over-sampling (SMOTE) technique to avoid over-fitting and under-fitting issues that result in biased classification. Several machine learning and deep learning algorithms have been implemented to classify data for binary and multi-class classification. We evaluate the efficacy of the proposed intelligent model on ToN_IoT dataset. The proposed approach achieved an accuracy rate of 99.99% and a reduced error rate of 0.001% for binary classification, and an accuracy rate of 99.98% and a reduced error rate of 0.016% for multi-class classification.