System and method for automated machine-learning, zero-day malware detection
US9665713B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 21, 2016 |
| Grant date | May 30, 2017 |
| Priority date | — |
| Expiry date | Mar 21, 2036 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2221/034
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Improved systems and methods for automated machine-learning, zero-day malware detection. Embodiments include a method for improved zero-day malware detection that receives a set of training files which are each known to be either malign or benign, partitions the set of training files into a plurality of categories, and trains category-specific classifiers that distinguish between malign and benign files in a category of files. The training may include selecting one of the plurality of categories of training files, identifying features present in the training files in the selected category of training files, evaluating the identified features to determine the identified features most effective at distinguishing between malign and benign files, and building a category-specific classifier based on the evaluated features. Embodiments also include by a system and computer-readable medium with instructions for executing the above method.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.