Advanced Strategies for Attack Detection and Mitigation in Industrial IoT: A Comprehensive Review

Abstract

The Industrial Internet of Things (IIoT) is transforming industries by enabling interconnected systems and real-time data analytics, driving efficiency and innovation across sectors such as manufacturing, energy, and transportation. However, the integration of IIoT also introduces significant cybersecurity risks, making industrial environments vulnerable to sophisticated cyber-attacks that can disrupt operations, cause physical damage, and compromise sensitive data. This paper provides a comprehensive review of advanced strategies for attack detection and mitigation in IIoT systems. It categorizes and analyzes various threat vectors unique to IIoT environments, including network intrusions, malware propagation, and insider threats. The paper examines state-of-the-art techniques such as machine learning-based anomaly detection, signature-based intrusion detection systems, and real-time threat intelligence sharing. Additionally, it explores emerging approaches like blockchain for secure data transactions, edge computing for decentralized threat mitigation, and artificial immune systems for adaptive defense mechanisms. By synthesizing the latest research and developments, this review aims to offer valuable insights for researchers, practitioners, and policymakers to enhance the resilience of IIoT systems against evolving cyber threats.