Cisco Technology, Inc.

Saurabh Verma

Antonio Nucci

Gyana Ranjan Dash

Manjula Shivanna

Sujit Biswas

Shamya Karumbaiah

Arvind Narayanan

Sequences of computer network log entries indicative of a cause of an event described in a first type of entry are identified by training a long short-term memory (LSTM) neural network to detect computer network log entries of a first type. The network is characterized by a plurality of ordered cells F=(x, c, h) and a final sigmoid layer characterized by a weight vector w. A sequence of log entries xis received. An hfor each entry is determined using the trained F. A value of gating function G(h, h)=II (w(h&#x2212;h)+b) is determined for each entry. II is an indicator function, b is a bias parameter. A sub-sequence of xcorresponding to G(h, h)=1 is output as a sequence of entries indicative of a cause of an event described in a log entry of the first type.

Neural network-assisted computer network management

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.

Spatio-temporal anomaly detection in computer networks using graph convolutional recurrent neural networks (GCRNNs)

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Minneapolis, MN, United States of America

Saurabh Verma