A deep learning perspective on Connected Automated Vehicle (CAV) cybersecurity and threat intelligence
Abstract
The connected and autonomous vehicle (CAV) is the next-generation mobility service—powered by intelligent automation and robust communication—which is aimed at replacing human-maneuvered vehicles with the software agent matching or even exceeding the human-level intelligence, control, and agility and minimizing errors. The next generation of transportation and mobility envisions safe, reliable, agile, automated, trustworthy, and service-based mobility architecture. The architecture should be able to eliminate human errors by using intelligent decision-making software agents based on the situational and behavioral information collected by sensors and transceivers through communication. Apart from that, service-based architecture removes the concept of vehicle ownership and incorporates more diversity in passengers, including the disabled and elderly people. CAV is the evolving technology to achieve the envisioned goals of future mobility and transportation. This chapter sheds light on cyber-physical vulnerabilities and risks that originated in informational technology (IT), operational technology (OT), and the physical domains of the CAV ecosystem, eclectic threat landscapes, and threat intelligence.
Publication Title
Deep Learning and Its Applications for Vehicle Networks
Recommended Citation
Basnet, M., & Ali, M. (2023). A deep learning perspective on Connected Automated Vehicle (CAV) cybersecurity and threat intelligence. Deep Learning and Its Applications for Vehicle Networks, 39-56. https://doi.org/10.1201/9781003190691-4
