Detecting behavioral change of IoT devices using novelty detection based behavior traffic modeling
US11888718B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 28, 2022 |
| Grant date | Jan 30, 2024 |
| Priority date | — |
| Expiry date | Jan 28, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
An anomalous behavior detector has been designed to detect novel behavioral changes of devices based on network traffic data that likely correlate to anomalous behaviors. The anomalous behavior detector uses the local outlier factor (LOF) algorithm with novelty detection. After initial semi-supervised training with a single class training dataset representing stable device behaviors, the obtained model continues learning frontiers that delimit subspaces of inlier observations with live network traffic data. Instead of traffic variables being used as features, the features that form feature vectors are similarities of network traffic variable values across time intervals. A feature vector for the anomalous behavior detector represents stability or similarity of network traffic variables that have been chosen as device identifiers and behavioral indicators.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.