  The Internet of Vehicles (IoV), whereby interconnected vehicles communicate
with each other and with road infrastructure on a common network, has promising
socio-economic benefits but also poses new cyber-physical threats. Data on
vehicular attackers can be realistically gathered through cyber threat
intelligence using systems like honeypots. Admittedly, configuring honeypots
introduces a trade-off between the level of honeypot-attacker interactions and
any incurred overheads and costs for implementing and monitoring these
honeypots. We argue that effective deception can be achieved through
strategically configuring the honeypots to represent components of the IoV and
engage attackers to collect cyber threat intelligence. In this paper, we
present HoneyCar, a novel decision support framework for honeypot deception in
IoV. HoneyCar builds upon a repository of known vulnerabilities of the
autonomous and connected vehicles found in the Common Vulnerabilities and
Exposure (CVE) data within the National Vulnerability Database (NVD) to compute
optimal honeypot configuration strategies. By taking a game-theoretic approach,
we model the adversarial interaction as a repeated imperfect-information
zero-sum game in which the IoV network administrator chooses a set of
vulnerabilities to offer in a honeypot and a strategic attacker chooses a
vulnerability of the IoV to exploit under uncertainty. Our investigation is
substantiated by examining two different versions of the game, with and without
the re-configuration cost to empower the network administrator to determine
optimal honeypot configurations. We evaluate HoneyCar in a realistic use case
to support decision makers with determining optimal honeypot configuration
strategies for strategic deployment in IoV.
