Binary Image Confidence Score for Secure Software
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Solution Overview
Problem
Determining the quality and robustness of binary software components is challenging due to the lack of direct analysis methods, leading to potential security breaches and system failures, especially in complex software systems like automotive environments where errors can be critical.
Innovation Solution
A method involving binary static analysis that dissects binary images into routines and functions, disassembles bytecode, retrieves local parameters, counts conditional routines, and creates a confidence score to assess the robustness and security of software components, allowing for the identification of secure binary images and determining their adherence to a secure model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If binary static analysis is performed to assess software quality and robustness, then the ability to identify security issues and potential failures is improved, but the complexity of the analysis process and computational resources required increase
Solution Approach 1:
The binary image is dissected into routines and functions, and further into basic blocks and instructions. This segmentation allows the complex analysis task to be broken down into manageable units, each can be analyzed independently using the same set of rules, making the overall complex process more systematic and computationally feasible.
Solution Approach 2:
The analysis transforms the binary image into a structured representation with specific parameters (routines, functions, basic blocks, instructions, parameters, conditionals). By changing the representation format from raw binary to structured data with defined parameters, the complexity of analysis is reduced while maintaining the ability to assess robustness.
2Reliability
If detailed static analysis of binary components is conducted to ensure security and robustness, then the detection of vulnerabilities is improved, but the time required for analysis increases
Solution Approach 1:
The analysis performs preliminary disassembly and structuring of the binary image into routines, functions, and basic blocks before conducting the actual robustness assessment. This preliminary action organizes the data in advance, allowing the subsequent analysis to proceed more efficiently through systematic evaluation of structured parameters rather than raw binary data.
Solution Approach 2:
The patent replaces complex mechanical interpretation of binary code with a systematic rule-based analysis approach. By substituting the complex task of understanding binary semantics with a standardized set of analysis rules applied to structured parameters, the analysis becomes more efficient and time-predictable while maintaining thorough security assessment.
3Reliability
If comprehensive analysis of all binary components is performed to ensure system-wide security, then the detection of system failures is improved, but the computational resources and processing load increase
Solution Approach 1:
The system divides the binary image into discrete routines and functions, each with associated parameters and conditionals. This segmentation enables parallel processing of different components, allowing comprehensive system-wide analysis to be distributed across multiple computational units, reducing the sequential processing time and overall computational resource consumption.
Solution Approach 2:
By transforming the binary image into a structured representation with standardized parameters (routines, functions, basic blocks, instructions, parameters, conditionals), the analysis creates a uniform data structure that can be processed efficiently. This parameter transformation enables consistent, scalable analysis across different binary components, optimizing computational resource utilization while maintaining comprehensive coverage.
Data Source
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AI summary
A method at a computing device for identification of secure binary images, the method including: dissecting a binary image into routines and functions; disassembling byte code for the dissected routines and functions; retrieving local routine and function parameters; counting a number of conditional routines for each local routine and function parameter; and creating a confidence score for each dissected routine and function.