Vehicle IDS and IVHM Integration for False Positive Filtering
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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 labor-intensive and inefficient monitoring process for security operations centers.
Innovation Solution
An integrated vehicle health management (IVHM) system is combined with cyber intrusion detection to differentiate between equipment failures and cyber-attacks using a symptom pattern recognition matrix initialized from vehicle design data, updated through a learning loop, and enhanced by neural networks for accurate anomaly classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an intrusion detection system (IDS) monitors vehicle networks for malicious activity using anomaly-based detection, then cyber-attack 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 IVHM system handles equipment failures by comparing anomalies against known failure patterns, while the IDS handles cyber-attacks by comparing against attack patterns. This segmentation prevents equipment failures from being misclassified as cyber-attacks, thereby reducing false positives while maintaining detection accuracy.
Solution Approach 2:
The IVHM system acts as an intermediary between the IDS and the anomaly analysis process. When the IDS detects an anomaly, the IVHM system first evaluates whether it represents an equipment failure by comparing it against the equipment failure pattern library. Only anomalies that the IVHM system cannot explain as equipment failures are passed to the IDS for cyber-attack analysis, serving as a filtering intermediary that reduces false positives.
2Reliability
If a security operations center manually examines all anomalies reported by IDS, then detection thoroughness is improved, but labor and time consumption increases significantly
Solution Approach 1:
The system performs preliminary automated analysis of all anomalies using the IVHM system before human examination. The IVHM system pre-evaluates anomalies against equipment failure patterns and either resolves them automatically or flags them for further IDS analysis. This preliminary action filters out the majority of equipment failure anomalies, reducing the workload for security operations center analysts while maintaining thorough detection of actual cyber-attacks.
Solution Approach 2:
The IVHM system provides self-service capability by automatically diagnosing and resolving equipment failure anomalies without human intervention. When an anomaly matches a known equipment failure pattern in the IVHM library, the system automatically identifies and resolves it, eliminating the need for manual security operations center examination of routine equipment issues and significantly reducing analysis time.
3Measurement precision
If a learning loop updates the IVHM system with investigation results, then system accuracy and customization improve, but system complexity increases
Solution Approach 1:
The system implements a feedback mechanism where investigation results from security operations center analysts are fed back into the IVHM system to update the equipment failure pattern library. When analysts investigate anomalies and identify new equipment failure patterns or refine existing ones, this knowledge is automatically incorporated into the IVHM system, improving future anomaly classification accuracy. The feedback loop enables continuous learning and adaptation without requiring complex manual reconfiguration.
Data Source
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 to known equipment failures. 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.