(untitled) (CAHIERS MAGELLANES-NS, 2024)
This project introduces an intelligent data-driven model employing machine learning to enhance the security of intra-vehicle communication networks. Given the heightened complexity and interconnectivity of modern vehicles, ensuring robust security measures is imperative. Through comprehensive data analysis, the model identifies normal communication patterns and anomalies, enabling proactive detection of potential security threats. Leveraging machine learning algorithms, the system dynamically adapts to evolving circumstances in real time, establishing a responsive and adaptive security framework. The integration of this model with existing intra-vehicle systems is seamless, preserving the efficiency of communication networks while fortifying against cybersecurity risks. Through rigorous testing and simulations, this project aims to demonstrate the efficacy of the proposed model in significantly strengthening the security of intra-vehicle communications, contributing to the development of safer and more resilient automotive systems.