Cyber-Physical System Security: AI-Driven Intrusion Detection for Industrial IoT

Abstract

Cyber-Physical Systems (CPS) form the backbone of Industrial IoT (IIoT) by integrating physical devices
with computational intelligence to enable autonomous decision-making in smart industries. However, the
convergence of operational technology (OT) and information technology (IT) introduces unprecedented
security risks, including advanced persistent threats (APTs), ransomware attacks, and Denial-of-Service
(DoS) attacks. Traditional security mechanisms struggle to counter these dynamic threats. This paper
proposes an AI-driven Intrusion Detection System (AI-IDS) for IIoT environments, leveraging machine
learning (ML) and deep learning (DL) techniques for real-time anomaly detection. The proposed model
combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for
feature extraction, sequential pattern recognition, and threat classification. Experimental evaluations on
benchmark datasets reveal that the AI-IDS achieves 97.8% detection accuracy and reduces false positive
rates to 2.3%, significantly outperforming conventional IDS systems.