Connected Vehicle Risk Scoring for Real-Time Threat Mitigation
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Solution Overview
Problem
Existing threat detection systems for connected and autonomous vehicles are limited in their ability to process large quantities of data from different sources, often failing to detect threats, mis-prioritize threats, or inefficiently process threats due to their reliance on batch analysis and lack of vehicle-specific detections.
Innovation Solution
A method and system for connected vehicle risk detection that computes risk scores for various behaviors associated with a connected vehicle based on its contextual state, aggregates these scores to determine an aggregated risk score, and triggers mitigation actions based on the determined risk level, utilizing machine learning to improve accuracy and inter-behavior risk factor tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing threat detectors use batch analysis for threat detection, then processing complexity is reduced, but detection speed and responsiveness to threats deteriorate
Solution Approach 1:
The system dynamically adapts its analysis approach by implementing both real-time processing for immediate threat detection and batch processing for comprehensive analysis. The risk detection system adjusts its operational mode based on threat levels and data accumulation, enabling flexible response to varying security conditions while maintaining both speed and thoroughness.
2Use of energy by moving object
If existing threat detectors analyze data from a single vehicle, then computational resources are conserved, but detection accuracy and root cause identification deteriorate
Solution Approach 1:
The system merges data from multiple connected vehicles into a centralized risk detection system. By combining telemetry data, behavioral patterns, and risk indicators across a fleet of vehicles, the system achieves more accurate threat detection and root cause identification while distributing computational load across the network infrastructure.
Solution Approach 2:
The risk detection system serves multiple vehicles simultaneously with a single analytical engine. The system processes data from various vehicle types and operational contexts, providing universal threat detection capabilities that benefit the entire fleet while optimizing resource utilization through shared computational infrastructure.
3Reliability
If existing threat detectors process large quantities of data from multiple sources, then comprehensive threat coverage is achieved, but processing efficiency and threat prioritization deteriorate
Solution Approach 1:
The system segments the large volume of incoming data into distinct categories including telemetry data, behavioral patterns, risk indicators, and contextual information. Each segment is processed through specialized analytical modules that evaluate specific threat dimensions, enabling efficient handling of comprehensive data sets while maintaining high processing throughput through parallel evaluation streams.
Data Source
AI summary
A system and method for connected vehicle risk detection are presented. The includes computing risk scores for a plurality of behaviors detected with respect to a connected vehicle, wherein each of the detected plurality of behaviors is associated with the connected vehicle based on a contextual vehicle state of the connected vehicle; aggregating the computed risk scores to determine an aggregated risk score; determining a risk level for the connected vehicle based on the aggregated risk score; and causing execution of at least one mitigation action based on the determined risk level.