Extreme Networks, Inc.

Markus Nispel

Donald Bruce Grosser, Jr

Donagh S Horgan

Patrick A Bosa

Jeffrey W Haskell

Chun Zhang

Giacomo Bernardi

Ernie Eaton

Systems and methods are disclosed herein for monitoring health of each switch of a plurality of switches on a network by selectively mirroring packets transmitted by each switch of the plurality of switches. In some embodiments, control circuitry generates a plurality of mirroring parameters, each mirroring parameter comprising an instruction to mirror a respective type of packet. The control circuitry transmits the plurality of mirroring parameters to each switch of the plurality of switches on the network, and receives, from a switch, a packet that was mirrored by the switch according to a mirroring parameter of the plurality of mirroring parameters. The control circuitry determines the respective type of the packet, executes an analysis of contents of the packet based on the respective type of the packet, and determines a health of the switch based on results of the analysis.

Systems and methods for determining flow and path analytics of an application of a network using sampled packet inspection

Disclosed herein are system, method, and computer program product embodiments for providing an anomaly detection system. Some aspects of this disclosure include a method for detecting anomaly in a network device. The method includes determining one or more similarity values between a flow vector corresponding to a flow associated with the network device and one or more flow clusters associated with the network device. The method further includes determining a maximum similarity value as a maximum of the one or more similarity values and comparing the maximum similarity value to a threshold. The method also includes, in response to the maximum similarity value being equal to or greater than the threshold, updating a flow cluster associated with the maximum similarity value. The method also includes, in response to the maximum similarity measure being less than the threshold, detecting the anomaly in the network device.

Online anomaly detection of vector embeddings

Systems and methods are disclosed herein for reducing storage space used in tracking behavior of a plurality of network endpoints by modeling the behavior with a behavior model. To this end, control circuitry may determine a respective network endpoint, of a plurality of network endpoints, to which each respective record of a plurality of received records corresponds. The control circuitry then may assign a dedicated queue for each respective network endpoint, and transmit, to each dedicated queue, each record that corresponds to the respective network endpoint to which the respective dedicated queue is assigned. The control circuitry may then determine, for each respective network endpoint, a respective behavior model, and may store each respective behavior model to memory.

Systems and methods for zero-footprint large-scale user-entity behavior modeling

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Boston, MA, United States of America

Markus Nispel