Human Centered AI and Cybersecurity: An Innovative Approach to Manage Cyber Risk in Transportation 5.0

Traffic management systems are increasingly utilizing IoT to develop smart urban transportation networks as part of intelligent transportation system (ITS) in Transportation 5.0. While this solution helps enhancing traffic efficiency, it also presents opportunities for attackers to intervene through various endpoints. Weak security or violations of access control can lead to significant cyber risks and economic disruptions. On the other hand, the solution cannot be fully automated, as it requires human intelligence to adjust configurations dynamically in response to changing events such as cyber threats, system updates and maintenance, traffic pattern changes, policy changes, user role changes, and performance optimization. This paper will explore an approach using Fuzzy Cognitive Maps (FCMs) and Artificial Intelligence (AI) to propose an effective framework in which humans can adjust causal settings based on the value of cyber assets, countermeasures, control types, processing methods, and assets' stability dependent on system vulnerabilities. These inputs will feed into AI module to make informed decisions by validating human decisions, and applying necessary corrections to address cyber risks proactively. The AI enablement will use various phases―start, analyze, decide, and act―to provide success and failure results based on FCM-optimized weights available for cyber assets. In this conceptual exploration, the focus of AI will remain on reducing cyber risk and creating awareness among humans about why human decisions are not appropriate. Furthermore, in the cases of false positives or negatives events affiliated with cyber risk identification, human-applied feedback will enable the AI to learn patterns and refine their future responses. With human awareness and artificial intelligence, we can achieve safer traffic management systems to strengthen ITS ecosystem in the realm of cybersecurity.