System and method for automatically generating neural networks for anomaly detection in log data from distributed systems
US11669735B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 23, 2020 |
| Grant date | Jun 6, 2023 |
| Priority date | — |
| Expiry date | Oct 10, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/082
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A system and method for automatically generating recurrent neural networks for log anomaly detection uses a controller recurrent neural network that generates an output set of hyperparameters when an input set of controller parameters is applied to the controller recurrent neural network. The output set of hyperparameters is applied to a target recurrent neural network to produce a child recurrent neural network with an architecture that is defined by the output set of hyperparameters. The child recurrent neural network is then trained, and a log classification accuracy of the child recurrent neural network is computed. Using the log classification accuracy, at least one of the controller parameters used to generate the child recurrent neural network is adjusted to produce a different input set of controller parameters to be applied to the controller recurrent neural network so that a different child recurrent neural network for log anomaly detection can be generated.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.