知搜-全球专利检索

  1. Login
  2. Sign Up

Search Results

27 records (took 0.024 seconds)

    Estimating feature confidence for online anomaly detection
    11.
    发明授权
    Estimating feature confidence for online anomaly detection 有权

    公开(公告)号：US10701092B2

    公开(公告)日：2020-06-30

    申请号：US15364440

    申请日：2016-11-30

    Applicant: Cisco Technology, Inc.

    Inventor： Laurent Sartran ,  Sébastien Gay ,  Jean-Philippe Vasseur ,  Grégory Mermoud

    IPC: H04L29/06

    Abstract: In one embodiment, a device in a network obtains characteristic data regarding one or more traffic flows in the network. The device incrementally estimates an amount of noise associated with a machine learning feature using bootstrapping. The machine learning feature is derived from the sampled characteristic data. The device applies a filter to the estimated amount of noise associated with the machine learning feature, to determine a value for the machine learning feature. The device identifies a network anomaly that exists in the network by using the determined value for the machine learning feature as input to a machine learning-based anomaly detector. The device causes performance of an anomaly mitigation action based on the identified network anomaly.

    HIERARCHICAL MODELS USING SELF ORGANIZING LEARNING TOPOLOGIES
    15.
    发明申请
    HIERARCHICAL MODELS USING SELF ORGANIZING LEARNING TOPOLOGIES 审中-公开

    公开(公告)号：US20190081973A1

    公开(公告)日：2019-03-14

    申请号：US16190756

    申请日：2018-11-14

    Applicant: Cisco Technology, Inc.

    Inventor： Pierre-André Savalle ,  Grégory Mermoud ,  Laurent Sartran ,  Jean-Philippe Vasseur

    IPC: H04L29/06 ,  H04L12/24

    CPC classification number: H04L63/1425 ,  H04L41/142 ,  H04L63/0236 ,  H04L63/1416 ,  H04L63/1458

    Abstract: In one embodiment, a device in a network maintains a plurality of anomaly detection models for different sets of aggregated traffic data regarding traffic in the network. The device determines a measure of confidence in a particular one of the anomaly detection models that evaluates a particular set of aggregated traffic data. The device dynamically replaces the particular anomaly detection model with a second anomaly detection model configured to evaluate the particular set of aggregated traffic data and has a different model capacity than that of the particular anomaly detection model. The device provides an anomaly event notification to a supervisory controller based on a combined output of the second anomaly detection model and of one or more of the anomaly detection models in the plurality of anomaly detection models.

    Specializing unsupervised anomaly detection systems using genetic programming
    16.
    发明授权
    Specializing unsupervised anomaly detection systems using genetic programming 有权

    公开(公告)号：US10218729B2

    公开(公告)日：2019-02-26

    申请号：US15205122

    申请日：2016-07-08

    Applicant: Cisco Technology, Inc.

    Inventor： Sébastien Gay ,  Laurent Sartran ,  Jean-Philippe Vasseur

    IPC: H04L29/06 ,  G06N99/00

    Abstract: In one embodiment, a device in a network receives sets of traffic flow features from an unsupervised machine learning-based anomaly detector. The sets of traffic flow features are associated with anomaly scores determined by the anomaly detector. The device ranks the sets of traffic flow features based in part on their anomaly scores. The device applies a genetic programming approach to the ranked sets of traffic flow features to generate new sets of traffic flow features. The genetic programming approach uses a fitness function that is based in part on the rankings of the sets of traffic flow features. The device specializes the anomaly detector to emphasize a particular type of anomaly using the new sets of traffic flow features.

    HIERARCHICAL MODELS USING SELF ORGANIZING LEARNING TOPOLOGIES
    18.
    发明申请
    HIERARCHICAL MODELS USING SELF ORGANIZING LEARNING TOPOLOGIES 有权

    公开(公告)号：US20220353285A1

    公开(公告)日：2022-11-03

    申请号：US17677541

    申请日：2022-02-22

    Applicant: Cisco Technology, Inc.

    Inventor： Pierre-André Savalle ,  Grégory Mermoud ,  Laurent Sartran ,  Jean-Philippe Vasseur

    IPC: H04L9/40 ,  H04L41/142

    Abstract: In one embodiment, a device obtains characteristics of a first anomaly detection model executed by a first distributed learning agent in a network. The device receives a query from a second distributed learning agent in the network that requests identification of a similar anomaly detection to that of a second anomaly detection model executed by the second distributed learning agent. The device identifies, after receiving the query from the second distributed learning agent, the first anomaly detection model as being similar to that of the second anomaly detection model, based on the characteristics of the first anomaly detection model. The device causes the first anomaly detection model to be sent to the second distributed learning agent for execution.

    HIERARCHICAL MODELS USING SELF ORGANIZING LEARNING TOPOLOGIES
    20.
    发明申请
    HIERARCHICAL MODELS USING SELF ORGANIZING LEARNING TOPOLOGIES 审中-公开

    公开(公告)号：US20200304530A1

    公开(公告)日：2020-09-24

    申请号：US16894332

    申请日：2020-06-05

    Applicant: Cisco Technology, Inc.

    Inventor： Pierre-André Savalle ,  Grégory Mermoud ,  Laurent Sartran ,  Jean-Philippe Vasseur

    IPC: H04L29/06 ,  H04L12/24

    Abstract: In one embodiment, a device obtains characteristics of a first anomaly detection model executed by a first distributed learning agent in a network. The device receives a query from a second distributed learning agent in the network that requests identification of a similar anomaly detection to that of a second anomaly detection model executed by the second distributed learning agent. The device identifies, after receiving the query from the second distributed learning agent, the first anomaly detection model as being similar to that of the second anomaly detection model, based on the characteristics of the first anomaly detection model. The device causes the first anomaly detection model to be sent to the second distributed learning agent for execution.

Patent Agency Ranking