Method and system for reducing false positives in static source code analysis reports using machine learning and classification techniques
US11620389B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 24, 2020 |
| Grant date | Apr 4, 2023 |
| Priority date | — |
| Expiry date | Jan 26, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
This invention is a computer-implemented method and system of using a secondary classification algorithm after using a primary source code vulnerability scanning tool to more accurately label true and false vulnerabilities in source code. The method and system use machine learning within a 10% dataset to develop a classifier model algorithm. A selection process identifies the most important features utilized in the algorithm to detect and distinguish the true and false positive findings of the static code analysis results. A personal identifier is used as a critical feature for the classification. The model is validated by experimentation and comparison against thirteen existing classifiers.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.