LLM-Based IDPS Rule Generation for Vehicle ECUs
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Solution Overview
Problem
Connected vehicles face challenges in generating accurate intrusion detection prevention system (IDPS) rules due to the complexity of their systems and the inefficiencies of traditional human-based and AI-generated approaches, leading to high false-positive rates and alert fatigue.
Innovation Solution
Utilizing a large language model (LLM) trained on threat assessment and remediation analysis (TARA) configurations and electronic/electrical architecture (EEA) specifications to generate IDPS rules, minimizing false positives and optimizing system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional human-based approach is used to develop IDPS rules, then accuracy of rules is improved, but development time and effort increase significantly
Solution Approach 1:
The system performs preliminary threat assessment and remediation analysis (TARA) to generate comprehensive security requirements and threat scenarios before IDPS rule development. This pre-analysis provides structured input that guides automated rule generation, reducing the need for manual expert analysis while maintaining accuracy.
Solution Approach 2:
An automated rule generation system acts as an intermediary between threat analysis requirements and final IDPS rules. This intermediary translates security requirements and threat scenarios into actionable IDPS rules automatically, bridging the gap between human expertise and automated generation without requiring direct human involvement in rule creation.
2Productivity
If AI-based approach is used to generate IDPS rules, then development speed is improved, but false-positive rate increases
Solution Approach 1:
The system incorporates feedback mechanisms where generated IDPS rules are evaluated against the comprehensive TARA analysis results and security requirements. This feedback loop validates rule accuracy and allows for iterative refinement, reducing false positives while maintaining rapid automated generation.
Solution Approach 2:
The rule generation process is segmented into multiple stages: threat scenario analysis, security requirement extraction, rule generation, and validation against TARA results. This segmentation allows each stage to focus on specific aspects, improving overall accuracy while maintaining automation speed.
3Reliability
If comprehensive IDPS rules are deployed to cover all threat scenarios, then security coverage is improved, but system overhead and performance impact increase
Solution Approach 1:
The system applies different levels of security monitoring and rule enforcement to different ECUs and threat scenarios based on their specific risk profiles. High-criticality ECUs receive comprehensive monitoring while lower-risk areas use lighter oversight, optimizing resource usage while maintaining security coverage.
Solution Approach 2:
The IDPS system dynamically adjusts monitoring parameters and rule enforcement intensity based on threat levels and system state. This allows the system to maintain comprehensive security coverage when needed while reducing overhead during normal operation, adapting to changing conditions.
Data Source
AI summary
A large language model is trained by inputting a TARA configuration of an ECU of a connected vehicle, an EEA specification, and IDPS rules of the ECU. Software modules are used to extract feature vectors for input to the LLM. The LLM is fine tuned by further inputting later versions of the ECU and data from other ECUs of the vehicle. IDPS rules for another ECU of the vehicle which has no rules or few rules are generated by inputting the TARA configuration of the other ECU into the trained LLM and prompting the LLM to generate rules. The prompt may include instructions to minimize overhead on the other ECU, reduce rules within the vehicle, etc. The generated rules are output from the LLM and stored within the IDPS engine of the other ECU, stored on a computer, transmitted to another vehicle. Rules for two other ECUs may be generated.