# HoneyCar: A Framework to Configure Honeypot Vulnerabilities on the Internet of Vehicles

arXiv: https://arxiv.org/abs/2111.02364 · HTML: https://mlsanity.com/paper/2111.02364/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 76 references that are in the map.
- First posted on arXiv 3 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Comparing Decision Support Approaches for Cyber Security Investment](https://mlsanity.com/paper/1502.05532/) (2015)
- [Post-Incident Audits on Cyber Insurance Discounts](https://mlsanity.com/paper/1908.04867/) (2019)
- [Enabling an Anatomic View to Investigate Honeypot Systems: A Survey](https://mlsanity.com/paper/1704.05357/) (2017)
- [Game theoretic path selection to support security in device-to-device communications](https://mlsanity.com/paper/1711.01447/) (2017)
- [Optimizing Investments in Cyber Hygiene for Protecting Healthcare Users](https://mlsanity.com/paper/2001.03782/) (2020)

## Built on by (most cited first, 1 of 1)

- [Cyber Deception: State of the art, Trends and Open challenges](https://mlsanity.com/paper/2409.07194/) (2024)

## Abstract (arXiv)

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.