Detecting unmanaged and unauthorized assets in an information technology network with a recurrent neural network that identifies anomalously-named assets
US11431741B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | May 13, 2019 |
| Grant date | Aug 30, 2022 |
| Priority date | — |
| Expiry date | Mar 13, 2041 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04W12/69
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
The present disclosure describes a system, method, and computer program for detecting unmanaged and unauthorized assets on an IT network by identifying anomalously-named assets. A recurrent neural network (RNN) is trained to identify patterns in asset names in a network. The RNN learns the character distribution patterns of the names of all observed assets in the training data, effectively capturing the hidden naming structures followed by a majority of assets on the network. The RNN is then used to identify assets with names that deviate from the hidden naming structures. Specifically, the RNN is used to measure the reconstruction errors of input asset name strings. Asset names with high reconstruction errors are anomalous since they cannot be explained by learned naming structures. After filtering for attributes or circumstances that mitigate risk, such assets are associated with a higher cybersecurity risk.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.