Centralized Cyber-Attack Detection for Connected Vehicles
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Solution Overview
Problem
Connected vehicles are vulnerable to cyber-attacks due to the collection and transmission of telemetric data, which can lead to vehicle misappropriation, failure, theft, and other malicious activities, posing risks to safety and financial security.
Innovation Solution
A method and system for detecting and mitigating cyber-attacks in connected vehicles by classifying data transmission behaviors as local or remote, identifying cyber-attack indicators, performing risk analysis by matching these indicators to known attack patterns, and implementing mitigation actions based on the analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computerized control and management systems collect and transmit telemetric data from vehicles, then vehicle monitoring and control capabilities are improved, but vehicles become vulnerable to cyber-attacks and malicious activities
Solution Approach 1:
The patent introduces a centralized detection system that acts as an intermediary between vehicles and the external network. This system analyzes telemetry data and detects cyber-attacks centrally, protecting individual vehicles while maintaining monitoring capabilities. The intermediary filters malicious traffic before it reaches vehicles, resolving the contradiction between connectivity and security.
Solution Approach 2:
The system performs preliminary risk analysis and attack detection by classifying behaviors and matching indicator combinations against known attack patterns before malicious actions can execute. By proactively identifying potential threats in telemetry data, the system prevents cyber-attacks while maintaining normal vehicle operations and monitoring functions.
2Measurement precision
If centralized detection systems analyze multiple cyber-attack indicators and perform risk analysis, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the detection process into distinct modules: behavior classification (local/remote), indicator determination, risk analysis with pattern matching, and mitigation actions. Each module handles specific aspects of detection independently, improving accuracy through comprehensive analysis while managing complexity through modular architecture. The segmented approach allows systematic processing of multiple indicators without overwhelming system complexity.
Data Source
AI summary
Systems and methods for detecting and mitigating cyber-attacks directed to connected vehicles. A method includes classifying a behavior of a connected vehicle into at least one classification with respect to a location of data transmission relative to the connected vehicle, wherein the at least one classification includes any of local and remote; determining a plurality of vehicle-related cyber-attack indicators related to the behavior of the connected vehicle; performing risk analysis based on a first combination of vehicle-related cyber-attack indicators and the classification, wherein performing the risk analysis further comprises matching the first combination to a plurality of second combinations of cyber-attack indicators of a plurality of known attack patterns, wherein each of the plurality of known attack patterns has at least one classification matching the at least one classification of the connected vehicle; and performing at least one mitigation action based on the risk analysis.