CYBER INTELLIGENT RISK ASSESSMENT FOR INDUSTRIAL IOT USING MACHINE LEARNING

Abstract

The rapid adoption of Industrial Internet of Things (IIoT) technologies has transformed traditional industrial environments by enabling interconnected devices, intelligent automation, and real-time data exchange. While these advancements improve operational efficiency and productivity, they also introduce significant cybersecurity challenges and vulnerabilities. Industrial IoT systems are increasingly exposed to cyber threats such as unauthorized access, malware attacks, data breaches, denial-of-service attacks, and system manipulation, which can disrupt critical industrial operations. This paper presents a Cyber Intelligent Risk Assessment framework for Industrial IoT using Machine Learning. The proposed approach leverages machine learning algorithms to analyze network traffic, device behavior, communication patterns, and security logs to identify potential cyber risks and vulnerabilities. By utilizing intelligent risk prediction and classification techniques, the system can proactively detect abnormal activities and assess threat levels in industrial environments. The integration of machine learning enhances risk assessment accuracy, reduces manual intervention, and enables adaptive security management. Experimental analysis demonstrates that the proposed framework effectively identifies cyber threats, improves risk prediction capabilities, and supports secure Industrial IoT operations. The developed model provides a scalable and intelligent solution for strengthening cybersecurity resilience in modern industrial ecosystems.