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Detecting Security-Relevant Methods using Multi-label Machine Learning
March 13, 2024, 4:42 a.m. | Oshando Johnson, Goran Piskachev, Ranjith Krishnamurthy, Eric Bodden
cs.LG updates on arXiv.org arxiv.org
Abstract: To detect security vulnerabilities, static analysis tools need to be configured with security-relevant methods. Current approaches can automatically identify such methods using binary relevance machine learning approaches. However, they ignore dependencies among security-relevant methods, over-generalize and perform poorly in practice. Additionally, users have to nevertheless manually configure static analysis tools using the detected methods. Based on feedback from users and our observations, the excessive manual steps can often be tedious, error-prone and counter-intuitive.
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abstract analysis analysis tools arxiv binary cs.lg current dependencies however identify machine machine learning practice security security vulnerabilities tools type vulnerabilities
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