IoV Reputation Defense Against Inside-Outside Collusion Attacks
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Solution Overview
Problem
In the Internet of Vehicles (IoV) system, malicious vehicles can manipulate reputation scores to deceive other vehicles, leading to misinformation and safety risks, as existing reputation systems are vulnerable to collusion attacks that exploit their scoring mechanisms.
Innovation Solution
A system and method utilizing a Reputation Fluctuation Association Analysis (RFAA) method, which includes a two-step processing message report and consumer rating, and a reputation fluctuation association rule to detect and prevent IOC attacks by analyzing reputation score fluctuations and association relationships between vehicles, thereby identifying and eliminating suspicious providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a reputation system is used to evaluate vehicle credibility, then information reliability is improved, but the system becomes vulnerable to collusion attacks where malicious vehicles manipulate reputation scores
Solution Approach 1:
The patent introduces an intermediary verification mechanism where RSUs act as neutral mediators to verify message authenticity and compute reputation scores. This intermediary layer prevents direct manipulation between vehicles, as the RSU validates information before it affects reputation calculations, thereby maintaining reliability while resisting collusion attacks.
Solution Approach 2:
The system implements continuous feedback loops where reputation scores are dynamically updated based on verified message histories and consumer ratings. This feedback mechanism allows the system to adapt to malicious behavior patterns, automatically adjusting credibility assessments to counteract collusion attempts while maintaining accurate information evaluation.
2Reliability
If reputation scores are continuously updated based on message verification, then information quality is improved, but computational overhead and system complexity increase
Solution Approach 1:
The patent divides the reputation management system into segmented functional modules: message verification by RSUs, separate reputation computation units, and independent consumer rating mechanisms. This segmentation allows each component to perform specialized tasks efficiently, improving information quality while managing system complexity through modular architecture.
Solution Approach 2:
The system performs preliminary verification of messages at the RSU level before they are incorporated into reputation calculations. By pre-validating information authenticity and pre-computing verification results, the system reduces the computational burden during reputation updates, maintaining high information quality without excessive processing overhead.
3Reliability
If all vehicle messages are verified before acceptance, then misinformation is reduced, but verification time increases which may delay critical safety responses
Solution Approach 1:
The patent implements partial verification where RSUs verify critical safety-related messages immediately while performing more comprehensive verification on non-critical information. This selective approach ensures that time-sensitive safety responses receive rapid verification to reduce misinformation without excessive delays, while less urgent messages undergo thorough validation.
Solution Approach 2:
The system performs preliminary trust establishment by verifying vehicle identities and message formats before full content validation. This preliminary action creates a fast initial verification layer that allows critical safety messages to be processed quickly, reducing verification time for urgent matters while maintaining comprehensive checks for other information types.
Data Source
AI summary
A system and method for securely defensing against a collusion attack under Internet of Vehicles (IoV) are provided. The present disclosure can repair a vulnerability, of a reputation system in the IoV, that the IOC attackers can manipulate a traffic-related message aggregation model (TMAM) by increasing their own reputation scores in an inside-and-outside collusion (IOC) manner. In addition, the present disclosure can detect IOC attacks quickly to improve the security of the IoV; can eliminate suspicious providers recursively and provide a reputation fluctuation association rule, to avoid overload of the TMAM; and can deprive IOC attackers of the opportunity to improve their reputation scores and ensure credible information in the IoV, to ensure the fairness and availability of the TMAM without the interference from the IOC attackers.