Automotive ECU Risk Analysis via Static-Dynamic Hybrid Node Profiling
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Solution Overview
Problem
Existing binary static analysis methods cannot fully determine attack surfaces, as they lack visibility into the processing characteristics of nodes within a system, which is crucial for identifying vulnerabilities and potential threats, especially in complex software environments like automotive systems.
Innovation Solution
Combining static and dynamic analysis techniques through a feedback loop to analyze binary images and message payloads, allowing for the identification of message identifiers and relative processing done by nodes, thereby enhancing the understanding of an attack surface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If binary static analysis is performed on software images, then analysis speed and efficiency are improved, but complete determination of attack surfaces is not achieved
Solution Approach 1:
The patent combines static analysis and dynamic analysis into a hybrid approach. Static analysis processes binary images to extract control flow graphs and basic blocks, while dynamic analysis executes the software to capture actual runtime behavior. The merging of these two analysis methods allows the system to maintain high analysis speed from static analysis while achieving complete attack surface determination through dynamic execution data, resolving the contradiction between speed and completeness.
Solution Approach 2:
The patent performs preliminary static analysis on binary images before dynamic execution to pre-process and structure the control flow information. By extracting control flow graphs, basic blocks, and instruction sequences in advance, the system prepares the data structure needed for efficient dynamic analysis, thereby maintaining high overall analysis speed while enabling complete attack surface determination during the subsequent dynamic phase.
2Measurement precision
If dynamic analysis is performed to completely determine attack surfaces, then measurement precision is improved, but analysis time and computational resources increase
Solution Approach 1:
The patent segments the analysis process into distinct phases: static analysis phase that processes binary images offline to extract control flow graphs and basic blocks, and dynamic analysis phase that executes software to capture runtime behavior. This segmentation allows the time-consuming static preprocessing to be performed separately, enabling the dynamic analysis to focus only on execution tracing, thereby reducing overall analysis time while maintaining complete attack surface determination.
Solution Approach 2:
The patent performs preliminary static analysis to pre-extract control flow graphs, basic blocks, and instruction sequences from binary images before dynamic execution. This preliminary preparation structures the data in advance, allowing the dynamic analysis phase to operate more efficiently by working with pre-processed information rather than raw binary data, thus reducing the time required for complete attack surface determination.
3Measurement precision
If comprehensive node processing characteristics are analyzed, then vulnerability identification accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex analysis task into manageable components: control flow graph extraction, basic block identification, instruction sequence analysis, and runtime behavior tracking. Each component is handled by specialized modules that process specific aspects of node behavior independently. This segmentation improves vulnerability identification accuracy by comprehensively analyzing multiple dimensions of node processing characteristics while managing system complexity through modular organization.
Solution Approach 2:
The patent introduces intermediary data structures such as control flow graphs, basic blocks, and instruction sequences that mediate between the raw binary software images and the final vulnerability assessment. These intermediaries simplify the analysis by providing structured representations of complex node behavior, enabling accurate vulnerability identification without requiring the entire system to simultaneously process all raw data, thus managing complexity while maintaining precision.
Data Source
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AI summary
A method at a computing device for determining processing characteristics of nodes within a system, the method including receiving at the computing device a plurality of messages being passed within the system; analyzing a payload for each message of the plurality of messages to determine one or more message identifiers; performing an analysis of a binary image for each node within the system to find nodes filtering for the one or more message identifiers; and determining a relative amount of processing done by each of the nodes based on the plurality of messages.