Connected Vehicle Misbehavior Protection via Context-Weighted Plausibility
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Solution Overview
Problem
Current connected vehicle systems lack a comprehensive and efficient method for recognizing and protecting against misbehavior, particularly in ensuring data-centric trust and detecting cyber attacks, which is crucial for maintaining network integrity and user safety.
Innovation Solution
A method involving onboard processors that perform plausibility determinations on received messages to generate misbehavior confidence indicators, utilizing multiple detection routines and context-based weighting to identify and address potential misbehavior, including cyber attacks, through a combination of hardware and software implementations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple plausibility determination routines are implemented to detect misbehavior, then misbehavior detection capability is improved, but system complexity increases
Solution Approach 1:
The misbehavior detection system is segmented into multiple independent plausibility determination routines, each focusing on specific aspects of message validity (e.g., temporal consistency, spatial plausibility, behavioral patterns). This segmentation allows comprehensive detection coverage while maintaining modular architecture that manages system complexity through organized functional divisions.
Solution Approach 2:
The onboard processor is designed to execute multiple different plausibility determination routines that can detect various types of misbehavior (cyber attacks, sensor failures, communication errors). This multi-functional approach enables a single system to handle diverse detection scenarios, improving overall reliability without requiring separate dedicated systems for each detection type.
2Measurement precision
If context-based weighting is applied to plausibility measurements, then detection accuracy is improved, but computational requirements increase
Solution Approach 1:
Context-based weighting applies different levels of scrutiny and weighting factors to different plausibility measurements based on local operating conditions (e.g., urban vs. rural environments, traffic density, weather conditions). This local quality approach optimizes detection accuracy for specific contexts while avoiding uniform high-computation processing in all situations, thereby managing energy requirements more efficiently.
Data Source
AI summary
The application is applicable for use in conjunction with a system that includes connected vehicle communications in which vehicles in the system each have an onboard processor subsystem and associated sensors, the processor subsystem controlling the generation, transmission, and receiving of messages communicated between vehicles for purposes including crash avoidance. A method is set forth for determining, by a given vehicle receiving messages, the occurrence of misbehavior, including the following steps: processing received messages by performing a plurality of plausibility determinations to obtain a respective number of plausibility measurements; determining at least one context for the region at which the given vehicle is located; weighting the plurality of plausibility measurements in accordance with values determined from the at least one context to obtain a respective plurality of plausibility indicator values; and deriving a misbehavior confidence indicator using the plausibility indicator values.