System and method for in-situ classifier retraining for malware identification and model heterogeneity
US10121108B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 8, 2016 |
| Grant date | Nov 6, 2018 |
| Priority date | — |
| Expiry date | Dec 24, 2036 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L63/1433
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A system and method for batched, supervised, in-situ machine learning classifier retraining for malware identification and model heterogeneity. The method produces a parent classifier model in one location and providing it to one or more in-situ retraining system or systems in a different location or locations, adjudicates the class determination of the parent classifier over the plurality of the samples evaluated by the in-situ retraining system or systems, determines a minimum number of adjudicated samples required to initiate the in-situ retraining process, creates a new training and test set using samples from one or more in-situ systems, blends a feature vector representation of the in-situ training and test sets with a feature vector representation of the parent training and test sets, conducts machine learning over the blended training set, evaluates the new and parent models using the blended test set and additional unlabeled samples, and elects whether to replace the parent classifier with the retrained version.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.