Industrial IoT–Edge Computing Security: A Comprehensive Review of Threats, Mitigation Strategies, and Future Directions

Abstract

Recent advancements in Industrial Internet of Things (IIoT) systems introduce two major trends: connecting with edge computing and the connectivity with Cyber-Physical Systems (CPS) that bring in real-time industrial automation, while exposing a much broader threat surface than conventional IT networks. This paper reviews a systematic survey that presents a comprehensive analysis of IIoT architectures, edge computing integration, CPS vulnerabilities, and a complete attack taxonomy spanning the perception, network, processing, and application layers. The review evaluates AI/ML-based intrusion detection systems, federated learning frameworks, blockchain-based audit mechanisms, cryptographic protocols, and Zero Trust Architecture as security countermeasures. Key findings confirm that advanced combinations of machine learning, federated learning, blockchain, and IDS are essential for data protection and real-time attack detection. Critical gaps are identified in standardized benchmarking environments, hardware-level security, and quantum-safe cryptographic migration pathways.