Building multi-representational learning models for static analysis of source code
US11615184B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 31, 2020 |
| Grant date | Mar 28, 2023 |
| Priority date | — |
| Expiry date | Apr 8, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2221/033
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A system/process/computer program product for building multi-representational learning models for static analysis of source code includes receiving training data, wherein the training data includes a set of source code files for training a multi-representational learning (MRL) model for classifying malicious source code and benign source code based on a static analysis; generating a first feature vector based on a set of characters extracted from the set of source code files; generating a second feature vector based on a set of tokens extracted from the set of source code files; and performing an ensemble of the first feature vector and the second feature vector to form a target feature vector for classifying malicious source code and benign source code based on the static analysis.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.