Hybrid Deep Learning-based Intrusion Detection System for Industrial Internet of Things

Abstract

The internet of things (IoT) is expected to offer a significant impact on the industry domain leading to the concept of industrial IoT (IIoT). The IIoT comprises machine-to-machine (M2M) and communication technologies with data automation and exchange to improve product quality and decrease pro-duction costs. As a consequence, a large amount of data is collected and smartly processed to provide optimal industrial operations. This growing deployment enables adversaries to con-duct potential and destructive cyber-attacks to accomplish their malicious goals. Therefore, intelligent decision-making actions for cyber-attack detection in IIoT are sorely required. To address this challenge, we propose an intrusion detection system (IDS) using deep learning models. Specifically, the proposed system is based on the combination of convolutional neural network (CNN) and long short-term memory (LSTM) that are excellent techniques for intrusion detection and classification due to their ability in classifying main characteristics and their effectiveness in performing faster computations. We adopt the most recent dataset named Edge-IIoTset that contains a real traffic network of IoT and IIoT applications. The proposed model is evaluated in terms of accuracy, precision, false positive rate, and detection cost within binary and multi-class classifications. The obtained results show that our CNN-LSTM model provides better performance and robustness in cyber security intrusion detection for IIoT applications compared to LSTM and traditional machine learning models. Moreover, it outperforms two recent related models in terms of accuracy rate.