Attention-Based Hybrid Deep Learning Model for Intrusion Detection in IIoT Networks

Abstract

Listen

Translate article

The integration of Industrial Internet of Things (IIoT) technology into the industrial sector has produced numerous significant advantages. However, the notable concern remains the absence of robust security and privacy measures in these interconnected critical environments. To secure IIoT networks, several researchers and experts employ intrusion detection systems (IDS) for detecting cyberattacks. The current systems exhibit efficient performance when handling a few categories of attack classes, even in the presence of slight imbalances. However, these models face challenges when confronted with vast categories of attack classes and highly imbalanced data. To tackle these issues, this study introduces an attention-based hybrid deep learning (AB-HDL) model designed to monitor network traffic and predict cyberattacks within the network. The proposed model comprises an attention mechanism and a hybrid deep learning model that integrates convolutional neural networks (CNN) and an autoencoder (AE). The effectiveness of the proposed AB-HDL is assessed using publicly accessible datasets: Edge-IIoTset and X-IIoTID. To ascertain the efficacy of AB-HDL, a comparative analysis is conducted with various other machine learning (ML) and deep learning (DL) algorithms. The outcome analysis indicates that the proposed AB-HDL surpasses the performance of the other algorithms and exhibits optimal efficiency in detecting cyber attacks within IIoT networks.