A comprehensive hybrid mathematical, deep learning, and IoT framework for industrial IT risks anticipation

The growing integration of IoT sensor networks in industrial environments has introduced critical cybersecurity concerns, necessitating more advanced and proactive risk anticipation methods. This study presents a hybrid predictive model that combines mathematical optimization techniques with deep neural network (DNN) architectures to forecast and mitigate ICT-related threats in industrial IoT systems. The mathematical component employs statistical analysis and graph-based modeling to assess vulnerabilities and evaluate potential risks. In parallel, the DNN analyzes temporal and behavioral patterns within sensor data to enhance threat recognition. This dual-approach model not only increases the accuracy of threat identification but also significantly reduces the rate of false positives, a persistent issue in conventional cybersecurity systems. Tested on real-world industrial datasets, the model demonstrates superior performance compared to existing approaches, particularly in terms of precision and reliability in risk anticipation. Its ability to adapt to dynamic and complex IoT network conditions further reinforces its relevance and robustness in modern industrial settings. By integrating the analytical strengths of mathematical modeling with the pattern recognition capabilities of deep learning, this framework offers a scalable and intelligent solution for industrial cybersecurity. It addresses current limitations in threat prediction and contributes to the development of more resilient, secure, and autonomous industrial