Fleet Cybersecurity Monitoring Through Distributed Vehicle Data Collection
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Solution Overview
Problem
The integration of computerized components in vehicles exposes them to cyber threats, necessitating a system and method to protect vehicles and fleets from cyber-attacks and risks.
Innovation Solution
A system and method involving data collection units in vehicles that collect cyber-security information, aggregate it, and send reports to a server for identifying cyber-attacks through correlation, analysis, and cross-referencing with stored data, logs, and databases, using geolocation and connectivity information to associate threats with locations and entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data collection units are installed in multiple vehicles to collect cyber security information, then the ability to identify cyber-attacks improves, but the system complexity and data processing requirements increase
Solution Approach 1:
The system divides the cyber-security monitoring function into distributed data collection units (DCUs) installed in individual vehicles, each independently collecting and preprocessing local cyber-security data. This segmentation allows the system to achieve comprehensive fleet-wide detection capability while keeping individual unit complexity manageable.
Solution Approach 2:
The server aggregates and combines data from multiple DCUs across the fleet, merging individual vehicle cyber-security information into a comprehensive view. This combining approach enables the system to identify fleet-wide attack patterns and correlate events that would be invisible at the individual vehicle level.
2Reliability
If comprehensive cyber security data is collected and aggregated from the fleet, then previously undetected threats can be identified, but false positive detections increase
Solution Approach 1:
The system uses aggregated fleet data as feedback to refine attack identification. By comparing individual vehicle events against the broader fleet context, the server can distinguish between isolated anomalies and coordinated attacks, reducing false positives while maintaining high detection capability.
Solution Approach 2:
The system adds the dimension of fleet-wide correlation to individual vehicle monitoring. By analyzing cyber-security events across multiple vehicles simultaneously, the system can identify attack patterns that span multiple vehicles and filter out isolated false alarms that don't match fleet-wide attack signatures.
3Speed
If real-time cyber security monitoring is implemented across the fleet, then response time to cyber-attacks improves, but the computational resources and energy consumption increase
Solution Approach 1:
DCUs continuously collect and pre-process cyber-security data in the background, maintaining readiness to detect attacks without requiring intensive real-time computation during normal operation. This preliminary data preparation enables rapid attack identification when threats occur while minimizing ongoing energy consumption.
Solution Approach 2:
The server acts as an intermediary that performs the intensive computational analysis of aggregated data, while DCUs in vehicles perform lighter data collection and transmission functions. This distribution of computational tasks reduces the energy burden on individual vehicles while maintaining real-time monitoring capability through centralized analysis.
Data Source
AI summary
A system and method for providing fleet cyber-security comprising may include collecting, by a plurality of data collection units installed in a respective plurality of vehicles in the fleet, information related to cyber security and including the information in reports to a server. Data in reports may be aggregated, by the server. A cyber-attack may be identified based on aggregated data.