Vehicle IDS-IVHM Correlation for False Alarm Reduction
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Solution Overview
Problem
Current intrusion detection systems (IDS) in vehicles suffer from high rates of false positives due to equipment failures being misinterpreted as cyber-attacks, leading to a deluge of anomalies that require labor-intensive analysis by security operations centers (SOC).
Innovation Solution
An integrated vehicle health management (IVHM) system is combined with cyber intrusion detection to create a symptom pattern recognition matrix that links anomaly patterns to known equipment failures and cyber-attacks, utilizing machine learning and neural networks to improve correlation and reduce false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If anomaly-based detection is used to monitor vehicle networks for cyber-attacks, then detection capability is improved, but false positive rate increases due to equipment failures being misinterpreted as cyber-attacks
Solution Approach 1:
The system segments anomaly analysis into two distinct pathways: equipment failure analysis and cyber-attack analysis. The symptom pattern recognition matrix is divided into separate sections for equipment failures and cyber-attacks, allowing each type of anomaly to be evaluated against appropriate reference patterns and reducing cross-contamination between the two detection domains.
Solution Approach 2:
The symptom pattern recognition matrix serves as an intermediary layer between raw anomaly detection and final classification. This matrix contains pre-established symptom patterns for both equipment failures and cyber-attacks, acting as a mediator that translates raw anomaly data into classified diagnostic outcomes, thereby improving classification accuracy.
2Reliability
If comprehensive anomaly monitoring is implemented across the vehicle network, then cyber-attack detection coverage is improved, but workload for security operations center increases due to deluge of false alarms
Solution Approach 1:
The system performs preliminary classification of anomalies using the symptom pattern recognition matrix before anomalies reach the security operations center. By pre-evaluating anomalies against known equipment failure patterns and cyber-attack patterns, the system filters out false positives in advance, reducing the workload and analysis time required at the SOC level.
Solution Approach 2:
The symptom pattern recognition matrix enables the system to self-classify anomalies without requiring extensive human intervention. The automated comparison of detected anomalies against stored symptom patterns allows the system to independently identify and categorize equipment failures versus potential cyber-attacks, reducing dependency on manual analysis.
3Measurement precision
If machine learning models are trained to distinguish equipment failures from cyber-attacks, then false positive reduction is improved, but system complexity increases
Solution Approach 1:
The symptom pattern recognition matrix is pre-populated with known symptom patterns for equipment failures and cyber-attacks during system initialization. This preliminary preparation of reference data eliminates the need for complex real-time machine learning training, simplifying the system architecture while maintaining high classification accuracy through pattern matching.
Data Source
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AI summary
An integrated vehicle health management (IVHM) system to resolve equipment-fault related anomalies detected by cyber intrusion detection system (IDS). A benefit of the present system is that it can result in fewer alerts that need manual analysis. A combination of cyber and monitoring with integrated vehicle health management (IVHM) may be a high value differentiator. As a solution gets more mature through a learning loop, it may be customized for different customers in a cost effective manner, something that might be expensive to develop on their own for most original equipment manufacturers (OEMs). An IVHM symptom pattern recognition matrix may link a pattern of reported symptoms (e.g., anomalies) to known equipment failures (electrical connections, sensors, controllers, known cyber-attacks, and so on). This matrix may be initialized from the vehicle design data but its entries may get updated by a learning loop that improves a correlation by incorporating results of investigations.