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Abstract
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The field of Cyber-Physical Industrial Internet of Things (CPS-IIoT) is rapidly developing, raising significant concerns about cyber-attacks due to the susceptibility of its devices and networking protocols. A breach in one device can compromise the entire system, necessitating robust security solutions. Existing methods fail to address the diversity and compatibility of CPS-IIoT environments. In this research, we propose a new privacy-preservation via Pearson correlation coefficient and agglomerative clustering with Bidirectional long short-term memory (BiLSTM) integrated with a scaled dot-product attention for cyberattacks detection in CPS-IIoT. The inclusion of agglomerative clustering and scaled dot product attention mechanism in our proposed system is a unique characteristic, specifically tailored for CPS-IIoT contexts. These mechanisms adaptively modify their emphasis to prioritize crucial features within the CPS-IIoT network traffic data, providing additional computational resources to data segments that are likely to include abnormalities and patterns that indicate security issues. This research is the first to investigate cyber-threats within CPS-Industrial IoT, differing from prior work on related systems. We evaluated the performance of our proposed model by conducting experiments on two relevant datasets: UNSW-NB15, and a novel IIoT dataset named X-IIoTID. The X-IIoTID is a versatile intrusion data designed to accommodate the diversity and compatibility of Industrial IoT systems, regardless of their connectivity and device specifications. The data encompasses the actions of emerging IIoT connectivity protocols, recent device activities, a range of attack kinds and situations, as well as multiple attack protocols. We use a CPS-IIoT testbed that emulates a real industrial facility to demonstrate our proof of concept. Our system exhibits outstanding performance attaining 99.60% of accuracy, 100% of AUC, 97.98% of recall, 100% of precision, F1 of 98.23%, kappa of 96.07%, and Mathew correlation coefficient of 96.54% on UNSW-NB15. On the representative and realistic X-IIoTID data, our proposed system exhibits outstanding performance attaining 99.99% of accuracy, 100% of AUC, recall of 99.97%, 99.98% of precision, 99.87% of F1-score, 99.97% of kappa, and Mathew correlation coefficient of 99.98%. Additionally, we design SHAPley Additive exPlanations from eXplainable AI to enhance our proposed model interpretability and reliability. The findings reveal that the scaled dot product attention mechanism dramatically boosts model performance, while Pearson correlation and agglomerative clustering safeguard data privacy in CPS-IIoT, surpassing the performance of existing stateof-the-art models.
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