Software-Defined Vehicle Fleet Management System with Integrated Cybersecurity Measures

Abstract

The significance of cybersecurity in today's globally linked world is paramount. Cybercriminals are finding new and more sophisticated ways to compromise fleet management systems, which regulate and track giant groupings of cars. The potential for cyberattacks is rising exponentially due to the increasing data-driven integration of various systems. Security threats, such as cyber vulnerabilities (CVs), have grown in tandem with the potential uses of extensive data-based communication in multiple sectors, including the autonomous car business. Data transmission between autonomous vehicles and Internet of Things devices may be more susceptible to cyberattacks because of the symmetry of extensive data communication networks employed by these vehicles. Both symmetric and asymmetric algorithms can encrypt the data associated with CVs. Proactive cybersecurity solutions for autonomous vehicles, power-based cyberattacks, and dynamic responses are among the many new concerns and opportunities presented by technological breakthroughs and shifting security threats. A lot of big data research has gone into finding ways to lessen the impact of CVs and big data breaches by implementing security solutions. Big data communication, autonomous vehicular networks (AVNs), and DCAVs will face future security challenges, primarily from CVs in data communication, vulnerabilities in AVMs, and cyber threats to network functioning. For this reason, security algorithms and countermeasure models must be efficient if CVs and data breaches are to be minimized. Integrating CV policies and rules with proxy and DMZ servers strengthened the countermeasure's effectiveness. Here, the energy levels of individual attacks are established to determine the information security measures that are reliant on the increasing degrees of assaults and CVs.