Automotive Cybersecurity Test Case Generation from Risk-Based Attack Vectors
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Solution Overview
Problem
Current test cases for automotive cybersecurity are not scientific and comprehensive, lacking objective test content, which poses a risk to networked automobiles and their users due to potential network attacks.
Innovation Solution
A method involving risk assessment, cluster analysis, Chi-square testing, and semantic processing to construct attack vectors and test cases, utilizing a TARA report for automotive cybersecurity detection, focusing on hazard impact, attack feasibility, and risk value to identify key risk areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional test case methods are used for automotive cybersecurity detection, then the testing process is simple, but the test content is not comprehensive and objective, leading to insufficient detection coverage
Solution Approach 1:
The patent segments the cybersecurity detection process into multiple dimensions including attack vectors, risk assessment criteria, and test case categories. By dividing the comprehensive test suite into modular components based on different attack scenarios and risk levels, the system achieves thorough coverage while maintaining manageable complexity through structured organization
Solution Approach 2:
The patent transforms abstract cybersecurity risks into quantifiable parameters by establishing a risk assessment model that converts qualitative threat information into measurable test criteria. This parameter transformation enables objective evaluation of detection coverage and allows for systematic generation of test cases based on quantified risk levels
2Measurement precision
If comprehensive risk assessment is performed on all attack vectors, then the detection accuracy is improved, but the testing time and cost increase significantly
Solution Approach 1:
The patent applies local quality by focusing testing resources on specific high-risk areas identified through the risk assessment model. Instead of uniformly testing all possible attack vectors, the system concentrates detailed analysis on critical components and pathways with higher risk scores, achieving accurate detection where it matters most while reducing overall testing time
Solution Approach 2:
The patent implements partial action by performing comprehensive risk assessment only on the most critical attack vectors and pathways. The system identifies and focuses on a subset of high-impact test cases that provide the greatest detection value, avoiding unnecessary testing of low-risk scenarios while maintaining high detection accuracy for critical threats
3Manufacturing precision
If detailed attack vector analysis is conducted for each risk scenario, then the test case quality is improved, but the complexity of test case generation increases
Solution Approach 1:
The patent uses copying by creating standardized test case templates for different attack vector patterns. Once a test case structure is developed for a specific attack scenario, it can be replicated and adapted for similar scenarios, maintaining high test case quality through consistent templates while reducing generation complexity through reuse of proven patterns
Solution Approach 2:
The patent applies preliminary action by pre-defining attack vector models, risk assessment criteria, and test case templates before actual testing begins. This advance preparation of structured frameworks and standardized patterns enables high-quality test case generation during execution while minimizing on-the-fly complexity
Data Source
AI summary
The disclosure provides a method, a system and a device for generating test case for automotive cybersecurity detection, which are mainly for regulatory detection of automotive cybersecurity, and applicable to automobile security detection and authentication based on an automotive cybersecurity development flow. The present disclosure constructs an attack vector by splitting an impact parameter of a risk to determine a vector value that contributes most to an automobile risk. The attack vector is dimensionally reduced by principal component analysis, so that the analysis is fast and operable. It constructs a key impact risk of each class by cluster analysis and Chi-square analysis in each class, to form a risk set. An attack path and a test case are constructed based on a risk set, so as to construct test case to be detected.