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- W4387191738 abstract "Binary code scoring analyzes a specified binary code region and provides an assessment of its weakness. Code weakness is not the same as software vulnerability; it represents the probability of future software vulnerabilities in the target code. Current vulnerability prediction research based on neural network models mostly focuses on directly locating vulnerable code positions. However, related studies have shown limitations in the neural network models' ability to learn binary code features, making it difficult to effectively distinguish subtle differences in code. Direct vulnerability detection using neural network models fails to identify vulnerabilities caused by processes such as code porting, functional adjustments, and patches. Additionally, due to the model's inherent limitations, it results in significant missed detections and false positives. To address these issues, this paper proposes a code scoring method based on semantic contribution. By utilizing the semantic function call graph (SemFCG) to represent code features and function call relationships, a matrix representation is achieved. The semantic attention model SemFCGAT is designed to learn semantic relationships between functions within SemFCG. Finally, code scoring is implemented based on the semantic contribution of neighboring functions to the central function. Experimental results demonstrate that our method can effectively score specified code regions, providing support for various software analysis tasks." @default.
- W4387191738 created "2023-09-30" @default.
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- W4387191738 date "2023-08-18" @default.
- W4387191738 modified "2023-09-30" @default.
- W4387191738 title "Semantic Contribution-based Binary Code Scoring" @default.
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- W4387191738 doi "https://doi.org/10.1109/icsece58870.2023.10263351" @default.
- W4387191738 hasPublicationYear "2023" @default.
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