Automatic identification of roles and connection anomalies

    公开(公告)号:US11799888B2

    公开(公告)日:2023-10-24

    申请号:US16434350

    申请日:2019-06-07

    CPC classification number: H04L63/1425 H04L41/069 H04L41/12 H04L41/28

    Abstract: A network topology analysis and validation system and technique are provided. In some implementations, the system may obtain information in real-time mode or as an off-line data set. The information being representative of a defined network topology type for a computer network and communication connections for devices within the computer network. The computer network analysis may be performed on a subnet of a larger network. A communication topology of the computer network may be compared with the expected (defined) network topology using bipartite and bi-colorization techniques to classify nodes of the computer network. After classification, anomalous communication connections (not in conformance with a standard for the defined network topology) may be identified and colored for presentation to a system administrator. Anomalous communication connections may initiate an alert, event, or alarm, via a system administration monitoring system for real-time notification.

    System for a flexible conductance crossbar

    公开(公告)号:US11200948B1

    公开(公告)日:2021-12-14

    申请号:US17005219

    申请日:2020-08-27

    Abstract: Systems are provided for implementing a hybrid resistor-memristor crossbar array, which allows for flexible conductance to be used in implementing the weight matrix of a neural network. The hybrid resistor-memristor crossbar array may include resistor crossbars, each resistor having a static conductance value. The hybrid resistor-memristor crossbar array may also have a memristor coupled to an output line associated with the resistor crossbar array, wherein the memristor has a variable conductance value, and further wherein the static conductance values and the variable conductance value are set to calculate a matrix-vector multiplication associated with a weight matrix of a neural network. An expected range of coefficients for a weight matrix of a neural network can be given by the Discrete Transform Cosine (DCT). Accordingly, the static conductance values of the resistors in the resistors crossbar array are set to values equal to known coefficients of the DCT.

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