Using ML Models and IOT to Secure Smart Vehicles from Relay Attacks

Abstract: This introduces an innovative approach to enhancing the security of smart vehicles by combining Machine Learning (ML) and the Internet of Things (IoT). The system utilizes IoT sensors to collect real-time data from the vehicle's environment and keyless entry system, which is then analyzed using ML algorithms to detect anomalies and potential relay attacks. To strengthen security, the system incorporates multi-factor authentication with biometric recognition such as fingerprint and facial recognition. Continuous learning and adaptation mechanisms ensure the system remains resilient to evolving threats, offering a robust defense against cyberattacks in smart vehicle environments. Through experimentation and validation, the system demonstrates its efficacy in accurately identifying and mitigating security threats, making it suitable for integration into existing automotive security frameworks.

Keywords: Keywords for securing smart vehicles from Relay attacks include IoT sensors, machine learning models, real-time monitoring, response mechanisms, Relay attacks, smart vehicles, security, detection, adaptability, resilience, continuous improvement, cyber threats, transportation, and digital age. Cite: Kumar Madar , Sweedal Flora Dmello, Yashwanth S, Anusha , Mr. Vijayananda V Madlur, "Using ML Models and IOT to Secure Smart Vehicles from Relay Attacks", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.133101.