中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

106 records (took 0.022 seconds)

    HIERARCHICAL NEURAL NETWORK-BASED ROOT CAUSE ANALYSIS FOR DISTRIBUTED COMPUTING SYSTEMS
    22.
    发明申请
    HIERARCHICAL NEURAL NETWORK-BASED ROOT CAUSE ANALYSIS FOR DISTRIBUTED COMPUTING SYSTEMS 有权

    公开(公告)号：US20220382614A1

    公开(公告)日：2022-12-01

    申请号：US17745134

    申请日：2022-05-16

    Applicant: NEC Laboratories America, Inc.

    Inventor： Zhengzhang Chen ,  Haifeng Chen ,  Yuncong Chen

    IPC: G06F11/07 ,  G06F11/34

    Abstract: Methods and systems for detecting and responding to an anomaly include determining a first system-level performance prediction using system-level statistics. A second system-level performance prediction is determined using system-level statistics and service-level statistics. The first prediction to the second prediction are compared to identify a discrepancy. It is determined that a service corresponding to the service-level statistics is a cause of a detected failure in a distributed computing system. An action directed to the service is performed responsive to the detected failure.

    VEHICLE INTELLIGENCE TOOL FOR EARLY WARNING WITH FAULT SIGNATURE
    24.
    发明申请
    VEHICLE INTELLIGENCE TOOL FOR EARLY WARNING WITH FAULT SIGNATURE 有权

    公开(公告)号：US20220084335A1

    公开(公告)日：2022-03-17

    申请号：US17464056

    申请日：2021-09-01

    Applicant: NEC Laboratories America, Inc. ,  NEC Corporation

    Inventor： LuAn Tang ,  Wei Cheng ,  Haifeng Chen ,  Yuji Kobayashi ,  Zhengzhang Chen

    IPC: G07C5/08 ,  G06N3/04

    Abstract: A method for early warning is provided. The method clusters normal historical data of normal cars into groups based on the car subsystem to which they belong. The method extracts (i) features based on group membership and (ii) feature correlations based on correlation graphs formed from the groups. The method trains an Auto-Encoder and Auto Decoder (AE&AD) model based on the features and the feature correlations to reconstruct the normal historical data with minimum reconstruction errors. The method reconstructs, using the trained AE&AD model, historical data of specific car fault types with reconstruction errors, normalizes the reconstruction errors, and selects features of the car faults with a top k large errors as fault signatures. The method reconstructs streaming data of monitored cars using the trained AE&AD model to determine streaming reconstruction errors, comparing the streaming reconstruction errors with the fault signatures to predict and provide alerts for impending known faults.

    STRUCTURAL GRAPH NEURAL NETWORKS FOR SUSPICIOUS EVENT DETECTION
    27.
    发明申请
    STRUCTURAL GRAPH NEURAL NETWORKS FOR SUSPICIOUS EVENT DETECTION 有权

    公开(公告)号：US20210067527A1

    公开(公告)日：2021-03-04

    申请号：US16992395

    申请日：2020-08-13

    Applicant: NEC Laboratories America, Inc.

    Inventor： Zhengzhang Chen ,  Jiaping Gui ,  Haifeng Chen ,  Lei Cai

    IPC: H04L29/06 ,  G06N3/08

    Abstract: A computer-implemented method for graph structure based anomaly detection on a dynamic graph is provided. The method includes detecting anomalous edges in the dynamic graph by learning graph structure changes in the dynamic graph with respect to target edges to be evaluated in a given time window repeatedly applied to the dynamic graph. The target edges correspond to particular different timestamps. The method further includes predicting a category of each of the target edges as being one of anomalous and non-anomalous based on the graph structure changes. The method also includes controlling a hardware based device to avoid an impending failure responsive to the category of at least one of the target edges.

    Graph model for alert interpretation in enterprise security system
    28.
    发明授权
    Graph model for alert interpretation in enterprise security system 有权

    公开(公告)号：US10885185B2

    公开(公告)日：2021-01-05

    申请号：US16161564

    申请日：2018-10-16

    Applicant: NEC Laboratories America, Inc.

    Inventor： LuAn Tang ,  Zhengzhang Chen ,  Zhichun Li ,  Zhenyu Wu ,  Jumpei Kamimura ,  Haifeng Chen

    IPC: H04L29/06 ,  G06F21/55 ,  H04L12/24 ,  G06F21/57

    Abstract: A computer-implemented method for implementing alert interpretation in enterprise security systems is presented. The computer-implemented method includes employing a plurality of sensors to monitor streaming data from a plurality of computing devices, generating alerts based on the monitored streaming data, automatically analyzing the alerts, in real-time, by using a graph-based alert interpretation engine employing process-star graph models, retrieving a cause of the alerts, an aftermath of the alerts, and baselines for the alert interpretation, and integrating the cause of the alerts, the aftermath of the alerts, and the baselines to output an alert interpretation graph to a user interface of a user device.

    PROTOCOL-INDEPENDENT ANOMALY DETECTION
    29.
    发明申请
    PROTOCOL-INDEPENDENT ANOMALY DETECTION 审中-公开

    公开(公告)号：US20200059484A1

    公开(公告)日：2020-02-20

    申请号：US16535521

    申请日：2019-08-08

    Applicant: NEC Laboratories America, Inc.

    Inventor： Junghwan Rhee ,  LuAn Tang ,  Zhengzhang Chen ,  Chung Hwan Kim ,  Zhichun Li ,  Ziqiao Zhou

    IPC: H04L29/06 ,  G05B19/418

    Abstract: A computer-implemented method for implementing protocol-independent anomaly detection within an industrial control system (ICS) includes implementing a detection stage, including performing byte filtering using a byte filtering model based on at least one new network packet associated with the ICS, performing horizontal detection to determine whether a horizontal constraint anomaly exists in the at least one network packet based on the byte filtering and a horizontal model, including analyzing constraints across different bytes of the at least one new network packet, performing message clustering based on the horizontal detection to generate first cluster information, and performing vertical detection to determine whether a vertical anomaly exists based on the first cluster information and a vertical model, including analyzing a temporal pattern of each byte of the at least one new network packet.

    Peer-based abnormal host detection for enterprise security systems
    30.
    发明授权
    Peer-based abnormal host detection for enterprise security systems 有权

    公开(公告)号：US10367842B2

    公开(公告)日：2019-07-30

    申请号：US15902318

    申请日：2018-02-22

    Applicant: NEC Laboratories America, Inc.

    Inventor： Zhengzhang Chen ,  LuAn Tang ,  Zhichun Li ,  Cheng Cao

    IPC: H04L29/06 ,  G06F21/55

    Abstract: Systems and methods for determining a risk level of a host in a network include modeling a target host's behavior based on historical events recorded at the target host. One or more original peer hosts having behavior similar to the target host's behavior are determined. An anomaly score for the target host is determined based on how the target host's behavior changes relative to behavior of the one or more original peer hosts over time. A security management action is performed based on the anomaly score.

Patent Agency Ranking