Security Threat Analysis and Cyber Attack Detection in Industrial IoT

Industrial Internet of Things (IIoT) has revolutionized modern manufacturing and critical infrastructure by enabling intelligent automation, real-time monitoring, and data-driven decision-making. However, the integration of cyber systems with physical industrial processes has introduced significant security challenges. Industrial IoT environments are highly vulnerable to cyber attacks such as Distributed Denial of Service (DDoS), ransomware, false data injection, malware propagation, and insider attacks, which can lead to severe financial losses and safety hazards. Traditional security mechanisms are often inadequate due to the heterogeneous, real-time, and resource-constrained nature of IIoT systems. This paper presents a comprehensive security threat analysis of Industrial IoT environments and proposes a machine learningbased cyber attack detection framework. The study systematically categorizes IIoT security threats and analyzes their impact on industrial operations. Various supervised and anomaly-based machine learning techniques are employed to detect malicious activities in industrial network traffic. Experimental evaluation using benchmark intrusion detection datasets demonstrates that machine learning-based approaches significantly improve attack detection accuracy while reducing false alarm rates. The results indicate that hybrid and ensemble models are highly effective for securing Industrial IoT infrastructures.