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- W4385730882 abstract "Software vulnerability detection is evolving from pattern-driven methods to data-driven methods to be more automatic and intelligent due to the emergence of deep learning. However, false positives and coarse-grained detection can not tackle changeable and unknown threats (e.g., Oday vulnerabilities), and also low model interpretability leads to incontinent detection results or maliciously being manipulated. To enhance model confidence and interpretability, in this paper, we propose an Interpretable vulnerability signature (IVSign) scheme to combine pattern-driven and data-driven methods. In IVSign, pattern-driven methods utilize static analysis to provide rich vulnerability trace information extracted from data dependencies and data-driven methods respectively train a transformer-based model to encode these vulnerability traces and a recurrent neural network to generate vulnerability signatures. In the testing stage of IVSign, if a program sample with vulnerabilities is fed into the pipeline, its hit rate in the signature database will be very high. Experiments have shown that when the matching threshold is set appropriately, the evaluation metrics on the testing samples exceed 90.00%." @default.
- W4385730882 created "2023-08-11" @default.
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- W4385730882 date "2023-06-01" @default.
- W4385730882 modified "2023-09-25" @default.
- W4385730882 title "IVSign: Interpretable Vulnerability Signature via Code Embedding and Static Analysis" @default.
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- W4385730882 doi "https://doi.org/10.1109/dsn-w58399.2023.00025" @default.
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