Vehicle Network Malicious Node Detection and Eviction
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Solution Overview
Problem
Existing vehicle communication networks face challenges in detecting and evicting malicious vehicles, especially in scenarios without infrastructure connectivity, where attackers can exploit vulnerabilities to cause harm and go undetected.
Innovation Solution
The proposed method combines voting and sacrifice principles using the Mafia Game mathematical model, allowing vehicles to determine the status of other vehicles as malicious or innocent and evict them from the network, thereby maintaining secure communication without relying on infrastructure networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicles rely on infrastructure network connectivity to detect malicious activities, then detection reliability is improved, but system dependency on infrastructure increases and operational autonomy decreases
Solution Approach 1:
The patent divides the malicious activity detection function into two segments: infrastructure-based detection (when available) and vehicle-based peer detection (when infrastructure is unavailable). Each segment operates independently based on connectivity conditions, allowing the system to maintain detection capability across different operational environments without complete infrastructure dependency.
Solution Approach 2:
Vehicles pre-establish detection capabilities and protocols before infrastructure connectivity is lost. The system prepares detection algorithms, message formats, and coordination mechanisms in advance, enabling immediate transition to autonomous vehicle-based detection without waiting for infrastructure failure to occur.
2Reliability
If vehicles implement comprehensive malicious behavior detection and eviction mechanisms, then network security is improved, but communication overhead and processing complexity increase
Solution Approach 1:
The patent implements differentiated detection strategies based on vehicle roles and threat levels. Not all vehicles perform identical comprehensive checks; instead, detection intensity and scope are adjusted locally based on observed behavior patterns, vehicle reputation, and current network conditions, reducing overall communication overhead while maintaining security.
Solution Approach 2:
The system performs detection actions selectively rather than continuously on all vehicles. Detection intensity is adjusted based on risk assessment - low-risk vehicles receive minimal monitoring while suspicious vehicles trigger more intensive scrutiny. This partial action approach maintains security without generating excessive communication traffic from uniform comprehensive checking of all nodes.
Data Source
AI summary
A method, computer program product and computer system maintain a list that includes identifiers of vehicles determined by the processor of a detective vehicle to have an innocent status. The processor selects, for each sub-period of a predefined time period, a target vehicle from a plurality of vehicles, where the detective vehicle communicates either directly or indirectly with each vehicle of the plurality and accepts communications from the plurality, where a portion of the plurality of vehicles and the detective vehicle are within a vehicle-to-vehicle communication range, and where vehicle-to-vehicle communication does not utilize infrastructure network connectivity. The processor obtains, over a vehicle-to-vehicle communication or a network connection, messages regarding a status of the target vehicle. The processor determines the status of the target vehicle, where the status is one of: malicious or innocent. The processor notifies the vehicles associated with the identifiers on the list of the determination.