Enhancing Security in Connected and Autonomous Vehicles: A Pairing Approach and Machine Learning Integration

Abstract

The automotive sector faces escalating security risks due to advances in wireless communication technology. Expanding on our previous research using a sensor pairing technique and machine learning models to evaluate IoT sensor data reliability, this study broadens its scope to address security concerns in Connected and Autonomous Vehicles (CAVs). The objectives of this research include identifying and mitigating specific security vulnerabilities related to CAVs, thereby establishing a comprehensive understanding of the risks these vehicles face. Additionally, our study introduces two innovative pairing approaches. The first approach focuses on pairing Electronic Control Units (ECUs) within individual vehicles, while the second extends to pairing entire vehicles, termed as vehicle pairing. Rigorous preprocessing of the dataset was carried out to ensure its readiness for subsequent model training. Leveraging Support Vector Machine (SVM) and TinyML methods for data validation and attack detection, we have been able to achieve an impressive accuracy rate of 97.2%. The proposed security approach notably contributes to the security of CAVs against potential cyber threats. The experimental setup demonstrates the practical application and effectiveness of TinyML in embedded systems within CAVs. Importantly, our proposed solution ensures that these security enhancements do not impose additional memory or network loads on the ECUs. This is accomplished by delegating the intensive cross-validation to the central module or Roadside Units (RSUs). This novel approach not only contributes to mitigating various security loopholes, but paves the way for scalable, efficient solutions for resource-constrained automotive systems.