Risk Assessment and Security of Industrial Internet of Things Network Using Advance Machine Learning

Securing IIoT networks is crucial for maintaining seamless operations, safeguarding sensitive industrial data, and averting safety risks. It helps managing financial exposure, protects intellectual property, and ensures compliance with regulations. Due to interconnected nature of IIoT devices, the looming threat of cyber incidents that could disrupt industries and supply chains. Machine learning is crucial for securing IIoT networks through tasks such as anomaly detection, predictive analytics, and adaptive threat response. By analyzing extensive datasets, it identifies patterns, detects deviations from normal behavior, and proactively addresses potential security threats, thereby fortifying the resilience and efficacy of IIoT network defenses. In this study, an optimized Gradient Boosting Decision Tree based model has been trained on a IIOT data to identify anomalies pattern and normal behavior. The trained model is tested and found efficient as compare to many machine learning model.