Collaborative Vehicular Threat Sharing: A Long-Term Contract-Based Incentive Mechanism With Privacy Preservation

The rapid development of the Internet of Vehicles (IoV) has spurred innovations in Intelligent Transportation Systems (ITS), but it also faces increasingly sophisticated cybersecurity threats. Traditional defense mechanisms often fall short in handling emerging and complex attacks due to the lack of flexibility to adapt to the rapidly evolving IoV environment. An emerging solution is to employ Large Language Models (LLMs), such as ChatGPT, to enhance IoV security, which depends on the quality, quantity, and freshness of the threat data used for fine-tuning. In this paper, we introduce a collaborative vehicular threat sharing framework that utilizes vehicular honeypots to gather threat data for fine-tuning LLMs, thereby bolstering IoV security. Local differential privacy is leveraged to safeguard the vehicles' privacy. Given that vehicles have different privacy preferences that may change over time, it is critical to design an appropriate incentive mechanism to encourage sustainable participation in the dynamic IoV environment. Moreover, since privacy preferences are the private information of the vehicles, an information asymmetry exists between the vehicles and the IDS cloud server. To address this challenge, we propose a dynamic contract-based incentive mechanism that considers the dynamically changing privacy preference during long-term participation. The optimal contract is derived to maximize the expected utility of the IDS cloud server. Extensive simulation results demonstrate the feasibility of our proposed dynamic contract based incentive mechanism and validate the effectiveness of the LLM-based threat classification in handling complex threats.