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What is Predicting Vulnerabilities with ML at ING about?

This document discusses predicting true vulnerabilities from static analyzer warnings in industry software using machine learning. It aims to reduce the time needed for developers and pentesters to manually verify warnings by static analyzers like Fortify, which often report many false positives. The study uses supervised machine learning classifiers and sampling techniques on code metric datasets from Fortify at two levels of granularity, class and method level, to determine which approach best predicts tr

Author

Ricardo Mansilla

Language

EN