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• W4286454954 abstract "Finding and eliminating security issues early in the development process is critical as software systems are shaping many aspects of our daily lives. There are numerous approaches for automatically detecting security vulnerabilities in the source code from which static analysis and machine learning based methods are the most popular. However, we lack comprehensive benchmarking of vulnerability detection methods across these two popular categories. In one of our earlier works, we proposed an ML-based line-level vulnerability prediction method with the goal of finding vulnerabilities in JavaScript systems. In this paper, we report results on a systematic comparison of this ML-based vulnerability detection technique with three widely used static checker tools NodeJSScan ( https://github.com/ajinabraham/nodejsscan ), ESLint ( https://eslint.org ), and CodeQL ( https://codeql.github.com ) using the OSSF CVE Benchmark ( https://github.com/ossf-cve-benchmark/ossf-cve-benchmark ). We found that our method was more than capable of finding vulnerable lines, managing to find 60% of all vulnerabilities present in the examined dataset, which corresponds to the best recall of all tools. Nonetheless, our method had higher false-positive rate and running time than that of the static checkers." @default.
• W4286454954 created "2022-07-22" @default.
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• W4286454954 date "2022-01-01" @default.
• W4286454954 modified "2023-09-29" @default.
• W4286454954 title "Comparing ML-Based Predictions and Static Analyzer Tools for Vulnerability Detection" @default.
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• W4286454954 doi "https://doi.org/10.1007/978-3-031-10542-5_7" @default.
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