Convoy Vehicle Intrusion Detection Using Adaptive Reference Data
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Solution Overview
Problem
Modern vehicles are vulnerable to network attacks due to increased network connectivity and complexity, posing safety threats, necessitating effective intrusion detection systems (IDS) that can detect and respond to such attacks.
Innovation Solution
A method and system utilizing an on-board unit (OBU) to analyze real-time data from vehicles in a convoy, comparing it with reference data to detect deviations and initiate operations when policy violations and deviations are detected, leveraging correlation analysis and machine learning to identify anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If intrusion detection systems collect and analyze all automotive data from vehicles, then detection accuracy improves, but data processing complexity and computational resources required increase
Solution Approach 1:
The system segments the data processing task by analyzing only specific data related to vehicle operations that are relevant for intrusion detection, rather than processing all automotive data. This selective analysis reduces computational complexity while maintaining detection accuracy for security-related anomalies.
Solution Approach 2:
The system uses an intermediary approach by comparing data from multiple vehicles in a convoy against each other and against reference data, rather than analyzing each vehicle's complete data set independently. This intermediary comparison method reduces the processing burden on individual vehicles while improving detection capability.
2Reliability
If the system compares real-time data from all vehicles in a convoy, then detection of intrusions improves, but communication bandwidth requirements and processing time increase
Solution Approach 1:
The system applies partial action by comparing only the necessary data elements from each vehicle that are relevant for intrusion detection, rather than processing complete data sets. This selective comparison reduces processing time while maintaining sufficient reliability for detecting security anomalies.
Solution Approach 2:
The system uses feedback mechanisms by continuously comparing real-time vehicle data against reference data and previous observations, allowing for adaptive processing that reduces time consumption through learned patterns while maintaining high detection reliability.
3Measurement precision
If the system uses correlation analysis and machine learning to detect anomalies, then false positives are reduced, but computational resources and processing complexity increase
Solution Approach 1:
The system changes parameters by adapting the complexity of correlation analysis and machine learning models based on the operational context and available computational resources. This dynamic parameter adjustment optimizes the balance between detection precision and energy consumption.
Solution Approach 2:
The system implements dynamics by making the analysis depth and computational intensity variable rather than fixed, allowing the system to adjust processing complexity based on real-time conditions while maintaining effective anomaly detection without excessive energy consumption.
4Adaptability or versatility
If the system implements continuous learning and adaptive policy adjustments, then detection accuracy improves, but system complexity and update frequency increase
Solution Approach 1:
The system applies preliminary action by pre-configuring learning mechanisms and policy frameworks that enable adaptive detection without requiring complex real-time reconfiguration. This preliminary setup allows the system to learn and adapt effectively while controlling overall system complexity.
Data Source
AI summary
A method executed by an on-board unit of a vehicle includes receiving real-time data from vehicles in a convoy, wherein the data includes an indicator regarding a vehicle type within the convoy, providing a policy with a set of rules and comparing the data with the policy, providing a set of reference data corresponding to the convoy, wherein the set of reference data includes a trend of previous data indicating normal operation of the convoy, comparing the set of data with the set of reference data, wherein a determination of whether the set of data deviates from the trend of previous data is based on operation of one of the vehicles in the convoy, and initiating an operation when there is both a violation of the policy and a deviation within the set of data from the set of reference data is detected.