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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. However, traditional ML and DL models encounter significant hurdles when faced with highly imbalanced training data and repetitive patterns within network datasets, hampering their performance in distinguishing between various classes of attacks. To overcome the challenges inherent in existing systems, this paper presents a self-attention-based deep …