PASTA Threat Modeling for Vehicular Networks Security

Vehicular networks are mobile ad hoc networks (MANET) that are mainly for vehicles, wireless and are known as VANETs. In a VANET, vehicles communicate with each other directly, without the need for a central infrastructure. This communication serves multiple purposes allowing vehicles to share information about their surroundings, their location, speed, direction of travel and other traffic information. Therefore securing the communication network and communication channels between the vehicles is increasingly important. VANETS are vulnerable to a variety of threats, and AI threats are becoming an increasing issue in particular. The more AI functionality is integrated into the vehicle, the more susceptible VANETS become to adverserial AI attacks. This paper provides a threat modeling approach for VANET networks using the PASTA threat model process focusing on AI attacks. This paper lists the different vulnerabilities VANETs exhibit, potential AI threats, and some mitigation techniques to protect VANETs from potential attacks.