Cisco Technology, Inc.

Gyana Ranjan Dash

Antonio Nucci

Donald Mark Allen

Manjula Shivanna

Konstantin Grechishchev

Kabeer Noorudeen

Tatiana Alexandrovna Gaponova

Carlos M Pignataro

Sujit Biswas

Saurabh Verma

Milind R Naphade

Qihong Shao

Dmitry Goloubew

Arvind Narayanan

Srinivas Ruddaraju

Pranjal Daga

Shamya Karumbaiah

Anastasia Feygina

In one example embodiment, a server that is in communication with a network that includes a plurality of network elements obtains, from the network, a service request record that includes sensitive information related to at least one of the plurality of network elements. The server parses the service request record to determine that the service request record includes a sequence of characters that is repeated in the service request record, and tags the sequence of characters as a particular sensitive information type. Based on the tagging, the server identically replaces the sequence of characters so as to preserve an internal consistency of the service request record. After identically replacing the sequence of characters, the server publishes the service request record for analysis without revealing the sequence of characters.

Automated data anonymization

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 computing device scans a plurality of available data sources associated with a profiled identity for an individual, and categorizes instances of the data sources according to recognized terms within the data sources. Once determining whether the profiled identity contributed positively to each categorized instance, categorized instances that have a positive contribution by the profiled identity may be clustered into clusters. The computing device may then rank the clusters based on size of the clusters and frequency of recognized terms within the clusters, and can then infer an expertise of the profiled identity based on one or more best-ranked clusters. The inferred expertise of the profiled identity may then be stored.

Dynamically inferred expertise

At a communication server, a first computer device and a second computer device are connected to a collaborative support session configured to support audio communications, screen sharing, and control of the first computer device by the second computer device. Screen sharing video image content is converted to a text sequence with timestamps. A text log with timestamps is generated from the text sequence. Using a command-based machine learning model, a sequence of commands and associated parameters, with timestamps, are determined from the text log. Audio is analyzed to produce speech-based information with timestamps. The command sequence is time-synchronized with the speech-based information based on the timestamps of the command sequence and the timestamps of the speech-based information. A knowledge report for the collaborative support session is generated. The knowledge report includes entries each including a timestamp, commands and associated parameters, and speech-based information that are time-synchronized to the timestamp.

Extracting knowledge from collaborative support sessions

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)

Growing community of inventors

San Jose, CA, United States of America

Gyana Ranjan Dash