Invention Grant
- Patent Title: Spatio-temporal anomaly detection in computer networks using graph convolutional recurrent neural networks (GCRNNs)
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Application No.: US15949198Application Date: 2018-04-10
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Publication No.: US10771488B2Publication Date: 2020-09-08
- Inventor: Saurabh Verma , Manjula Shivanna , Gyana Ranjan Dash , Antonio Nucci
- Applicant: Cisco Technology, Inc.
- Applicant Address: US CA San Jose
- Assignee: Cisco Technology, Inc.
- Current Assignee: Cisco Technology, Inc.
- Current Assignee Address: US CA San Jose
- Agency: Behmke Innovation Group LLC
- Agent Kenneth J. Heywood; Jonathon P. Western
- Main IPC: H04L29/06
- IPC: H04L29/06 ; G06N3/08

Abstract:
In one embodiment, a device receives sensor data from a plurality of nodes in a computer network. The device uses the sensor data and a graph that represents a topology of the nodes in the network as input to a graph convolutional neural network. The device provides an output of the graph convolutional neural network as input to a convolutional long short-term memory recurrent neural network. The device detects an anomaly in the computer network by comparing a reconstruction error associated with an output of the convolutional long short-term memory recurrent neural network to a defined threshold. The device initiates a mitigation action in the computer network for the detected anomaly.
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