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The Industrial Internet of Things (IIoT) comprises a variety of systems, smart devices, and an extensive range of communication protocols. Hence, these systems face susceptibility to privacy and security challenges, making them prime targets for malicious attacks that can result in harm to the overall system. Privacy breach issues are a notable concern within the realm of IIoT. Various intrusion detection systems based on machine learning (ML) and deep learning (DL) have been introduced to detect malicious activities within these networks and identify attacks. The existing ML and DL-based models face challenges when confronted with highly imbalanced training. Repetitive data in network datasets inflates model performance, as the model has encountered much of the test set data during training. Moreover, these models decrease performance when confronted with datasets that include repetitions of similar data across various classes, where only the class labels are different. To overcome the challenges inherent in existing systems, this paper presents a self-attention-based deep convolutional neural network (SA-DCNN) model designed for monitoring the IIoT networks and detecting malicious activities. Additionally, a two-step cleaning method has been implemented to eliminate redundancy within the training data, considering both intra-class and cross-class samples. The performance of the SA-DCNN model is assessed using IoTID20 and Edge-IIoTset datasets. Furthermore, the proposed study is demonstrated through a comprehensive comparison with other ML and DL models, as well as against relevant studies, showcasing the superior performance and efficacy of the proposed model.

A Self-Attention-Based Deep Convolutional Neural Networks for IIoT Networks Intrusion Detection