Multi-representational learning models for static analysis of source code
US11783035B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 15, 2022 |
| Grant date | Oct 10, 2023 |
| Priority date | — |
| Expiry date | Nov 15, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/08
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques for multi-representational learning models for static analysis of source code are disclosed. In some embodiments, a system/process/computer program product for multi-representational learning models for static analysis of source code includes receiving at a networked device a set comprising one or more multi-representation learning (MRL) models for static analysis of source code; performing a static analysis of source code associated with a sample received at the network device, wherein performing the static analysis includes using at least one MRL model; and determining that the sample is malicious based at least in part on the static analysis of the source code associated with the sample and without performing dynamic analysis of the sample, and in response to determining that the sample is malicious, performing an action based on a security policy.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.