Threat Detection and Mitigation in the Realm of Connected Vehicle Systems

Abstract

Connected Vehicle Systems (CVS) are a combination of transportation and digital technologies that have the potential to revolutionize road safety and efficiency. However, this interconnectivity exposes them to various evolving cyber threats that require proactive detection and mitigation strategies. This study examines the security threat landscape in CVS, focusing on the challenges posed by malicious intrusions, unauthorized access, and vulnerabilities within vehicular networks. By using Deep Neural Networks (DNNs) and conducting an extensive literature review on cybersecurity frameworks, autonomous vehicles, and network vulnerabilities, this research provides a robust methodology for detecting and mitigating attacks in vehicular networks. The results show that the proposed approach is effective with improved predictive capabilities as well as the ability to detect abnormal behaviors. The findings highlight the need for standardized cybersecurity frameworks, cooperation among stakeholders, and continuous improvement of security protocols to ensure safe interconnected vehicular networks in a rapidly changing technological environment.