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    Deploying network anomaly detection systems based on endpoint criticality
    1.
    发明授权
    Deploying network anomaly detection systems based on endpoint criticality 有权

    公开(公告)号：US12155526B1

    公开(公告)日：2024-11-26

    申请号：US18196705

    申请日：2023-05-12

    Applicant: Cisco Technology, Inc.

    Inventor： Sofia Karygianni ,  Andrea Di Pietro ,  Sukrit Dasgupta

    IPC: G06F15/173 ,  H04L41/0681 ,  H04L41/22

    Abstract: In one embodiment, a device determines a criticality of each of a plurality of endpoints in a network, based on network telemetry data regarding the network. The device translates a plurality of anomaly detection models available for deployment to the network and their metadata into a set of adjustable resources. The device generates an anomaly detection deployment strategy for the network by selecting a set of one or more of the plurality of anomaly detection models for deployment to one or more execution points in the network, based on the criticality of each of the plurality of endpoints and on the set of adjustable resources. The device causes the set to be deployed to the one or more execution points in the network, in accordance with the anomaly detection deployment strategy.

    Model training for on-premise execution in a network assurance system
    2.
    发明授权
    Model training for on-premise execution in a network assurance system 有权

    公开(公告)号：US11070441B2

    公开(公告)日：2021-07-20

    申请号：US16578565

    申请日：2019-09-23

    Applicant: Cisco Technology, Inc.

    Inventor： Andrea Di Pietro

    IPC: H04L12/24 ,  H04L29/12 ,  G06N20/00

    Abstract: In one embodiment, a network assurance service maintains a data lake of network telemetry data obtained by the service from any number of computer networks. The service generates a machine learning model for on-premise execution in a particular computer network to detect network issues in the particular network. To do so, the service repeatedly selects a candidate set of model settings based in part on the data lake of network telemetry data, trains a machine learning model using network telemetry data from the data lake that matches the candidate set of model settings, and tests performance of the trained model using an emulator that emulates network issues in the particular network. The service further deploys the generated machine learning model to the particular computer network for on-premise execution.

    Behavioral white labeling
    8.
    发明授权
    Behavioral white labeling 有权

    公开(公告)号：US09900342B2

    公开(公告)日：2018-02-20

    申请号：US14338582

    申请日：2014-07-23

    Applicant: Cisco Technology, Inc.

    Inventor： Javier Cruz Mota ,  Jean-Philippe Vasseur ,  Andrea Di Pietro

    IPC: H04L29/06

    CPC classification number: H04L63/1458 ,  H04L63/1416

    Abstract: In one embodiment, a traffic model manager node receives data flows in a network and determines a degree to which the received data flows conform to one or more traffic models classifying particular types of data flows as non-malicious. If the degree to which the received data flows conform to the one or more traffic models is sufficient, the traffic model manager node characterizes the received data flows as non-malicious. Otherwise, the traffic model manager node provides the received data flows to a denial of service (DoS) attack detector in the network to allow the received data flows to be scanned for potential attacks.

    SANITY CHECK OF POTENTIAL LEARNED ANOMALIES
    10.
    发明申请
    SANITY CHECK OF POTENTIAL LEARNED ANOMALIES 审中-公开

    公开(公告)号：US20170279832A1

    公开(公告)日：2017-09-28

    申请号：US15184252

    申请日：2016-06-16

    Applicant: Cisco Technology, Inc.

    Inventor： Andrea Di Pietro ,  Jean-Philippe Vasseur

    IPC: H04L29/06 ,  H04L29/12

    CPC classification number: H04L63/1425 ,  H04L61/1511 ,  H04L61/25 ,  H04L63/0281 ,  H04L63/1416 ,  H04L63/1458 ,  H04L63/1466

    Abstract: In one embodiment, a device in a network receives, from a supervisory device, trace information for one or more traffic flows associated with a particular anomaly. The device remaps network addresses in the trace information to addresses of one or more nodes in the network based on roles of the one or more nodes. The device mixes, using the remapped network addresses, the trace information with traffic information regarding one or more observed traffic flows in the network, to form a set of mixed traffic information. The device analyzes the mixed traffic information using an anomaly detection model. The device provides an indication of a result of the analysis of the mixed traffic information to the supervisory device.

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