中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

76 records (took 0.024 seconds)

    Deep graph de-noise by differentiable ranking
    41.
    发明授权
    Deep graph de-noise by differentiable ranking 有权

    公开(公告)号：US11645540B2

    公开(公告)日：2023-05-09

    申请号：US16936600

    申请日：2020-07-23

    Applicant: NEC Laboratories America, Inc.

    Inventor： Bo Zong ,  Cheng Zheng ,  Haifeng Chen

    IPC: G06V10/774 ,  G06K9/62 ,  G06F11/07 ,  G06N3/04 ,  G06V30/196

    CPC classification number: G06V10/7747 ,  G06F11/0766 ,  G06K9/6223 ,  G06K9/6257 ,  G06K9/6276 ,  G06N3/0454 ,  G06V30/1988

    Abstract: A method for employing a differentiable ranking based graph sparsification (DRGS) network to use supervision signals from downstream tasks to guide graph sparsification is presented. The method includes, in a training phase, generating node representations by neighborhood aggregation operators, generating sparsified subgraphs by top-k neighbor sampling from a learned neighborhood ranking distribution, feeding the sparsified subgraphs to a task, generating a prediction, and collecting a prediction error to update parameters in the generating and feeding steps to minimize an error, and, in a testing phase, generating node representations by neighborhood aggregation operators related to testing data, generating sparsified subgraphs by top-k neighbor sampling from a learned neighborhood ranking distribution related to the testing data, feeding the sparsified subgraphs related to the testing data to a task, and outputting prediction results to a visualization device.

    CROSS-LINGUAL ZERO-SHOT TRANSFER VIA SEMANTIC AND SYNTHETIC REPRESENTATION LEARNING
    42.
    发明申请
    CROSS-LINGUAL ZERO-SHOT TRANSFER VIA SEMANTIC AND SYNTHETIC REPRESENTATION LEARNING 有权

    公开(公告)号：US20220075945A1

    公开(公告)日：2022-03-10

    申请号：US17464005

    申请日：2021-09-01

    Applicant: NEC Laboratories America, Inc.

    Inventor： Xuchao Zhang ,  Yanchi Liu ,  Bo Zong ,  Wei Cheng ,  Haifeng Chen ,  Junxiang Wang

    IPC: G06F40/284 ,  G06F40/205 ,  G06F40/295 ,  G06N3/04

    Abstract: A computer-implemented method is provided for cross-lingual transfer. The method includes randomly masking a source corpus and a target corpus to obtain a masked source corpus and a masked target corpus. The method further includes tokenizing, by pretrained Natural Language Processing (NLP) models, the masked source corpus and the masked target corpus to obtain source tokens and target tokens. The method also includes transforming the source tokens and the target tokens into a source dependency parsing tree and a target dependency parsing tree. The method additionally includes inputting the source dependency parsing tree and the target dependency parsing tree into a graph encoder pretrained on a translation language modeling task to extract common language information for transfer. The method further includes fine-tuning the graph encoder and a down-stream network for a specific NLP down-stream task.

    Network gateway spoofing detection and mitigation
    43.
    发明授权
    Network gateway spoofing detection and mitigation 有权

    公开(公告)号：US10999323B2

    公开(公告)日：2021-05-04

    申请号：US16101834

    申请日：2018-08-13

    Applicant: NEC Laboratories America, Inc.

    Inventor： Cristian Lumezanu ,  Nipun Arora ,  Haifeng Chen ,  Bo Zong ,  Daeki Cho ,  Mingda Li

    IPC: G06F21/00 ,  H04L29/06 ,  H04L12/733 ,  H04L12/26 ,  H04L12/741 ,  G06N20/00 ,  H04L12/751 ,  H04L12/893 ,  G06K9/62 ,  G06N3/08

    Abstract: Endpoint security systems and methods include a distance estimation module configured to calculate a travel distance between a source Internet Protocol (IP) address and an IP address for a target network endpoint system from a received packet received by a network gateway system based on time-to-live (TTL) information from the received packet. A machine learning model is configured to estimate an expected travel distance between the source IP address and the target network endpoint system IP address based on a sparse set of known source/target distances. A spoof detection module is configured to determine that the received packet has a spoofed source IP address based on a comparison between the calculated travel distance and the expected travel distance. A security module is configured to perform a security action at the network gateway system responsive to the determination that the received packet has a spoofed source IP address.

    NODE CLASSIFICATION IN DYNAMIC NETWORKS USING GRAPH FACTORIZATION
    45.
    发明申请
    NODE CLASSIFICATION IN DYNAMIC NETWORKS USING GRAPH FACTORIZATION 有权

    公开(公告)号：US20210067558A1

    公开(公告)日：2021-03-04

    申请号：US17004547

    申请日：2020-08-27

    Applicant: NEC Laboratories America, Inc.

    Inventor： Jingchao Ni ,  Haifeng Chen ,  Bo Zong ,  LuAn Tang ,  Wei Cheng

    IPC: H04L29/06 ,  G06N3/04

    Abstract: Methods and systems for detecting and responding to anomalous nodes in a network include inferring temporal factors, using a computer-implemented neural network, that represent changes in a network graph across time steps, with a temporal factor for each time step depending on a temporal factor for a previous time step. An invariant factor is inferred that represents information about the network graph that does not change across the time steps. The temporal factors and the invariant factor are combined into a combined temporal-invariant representation. It is determined that an unlabeled node is anomalous, based on the combined temporal-invariant representation. A security action is performed responsive to the determination that unlabeled node is anomalous.

    Recommender system for heterogeneous log pattern editing operation
    46.
    发明授权
    Recommender system for heterogeneous log pattern editing operation 有权

    公开(公告)号：US10929763B2

    公开(公告)日：2021-02-23

    申请号：US15684293

    申请日：2017-08-23

    Applicant: NEC Laboratories America, Inc.

    Inventor： Jianwu Xu ,  Biplob Debnath ,  Bo Zong ,  Hui Zhang ,  Guofei Jiang ,  Hancheng Ge

    IPC: G06N5/04 ,  G06F17/16 ,  G06N7/00 ,  G06F40/00 ,  G06F40/16 ,  G06F40/284

    Abstract: A heterogeneous log pattern editing recommendation system and computer-implemented method are provided. The system has a processor configured to identify, from heterogeneous logs, patterns including variable fields and constant fields. The processor is also configured to extract a category feature, a cardinality feature, and a before-after n-gram feature by tokenizing the variable fields in the identified patterns. The processor is additionally configured to generate target similarity scores between target fields to be potentially edited and other fields from among the variable fields in the heterogeneous logs using pattern editing operations based on the extracted category feature, the extracted cardinality feature, and the extracted before-after n-gram feature. The processor is further configured to recommend, to a user, log pattern edits for at least one of the target fields based on the target similarity scores between the target fields in the heterogeneous logs.

    Discovering critical alerts through learning over heterogeneous temporal graphs
    48.
    发明授权
    Discovering critical alerts through learning over heterogeneous temporal graphs 有权

    公开(公告)号：US10409669B2

    公开(公告)日：2019-09-10

    申请号：US15810960

    申请日：2017-11-13

    Applicant: NEC Laboratories America, Inc.

    Inventor： Bo Zong ,  LuAn Tang ,  Qi Song ,  Biplob Debnath ,  Hui Zhang ,  Guofei Jiang

    IPC: G06F11/07 ,  G06F11/34 ,  G06N3/08 ,  G06N5/04 ,  G06N5/02

    Abstract: A method is provided that includes transforming training data into a neural network based learning model using a set of temporal graphs derived from the training data. The method includes performing model learning on the learning model by automatically adjusting learning model parameters based on the set of the temporal graphs to minimize differences between a predetermined ground-truth ranking list and a learning model output ranking list. The method includes transforming testing data into a neural network based inference model using another set of temporal graphs derived from the testing data. The method includes performing model inference by applying the inference and learning models to test data to extract context features for alerts in the test data and calculate a ranking list for the alerts based on the extracted context features. Top-ranked alerts are identified as critical alerts. Each alert represents an anomaly in the test data.

    UNSUPERVISED SPOOFING DETECTION FROM TRAFFIC DATA IN MOBILE NETWORKS
    49.
    发明申请
    UNSUPERVISED SPOOFING DETECTION FROM TRAFFIC DATA IN MOBILE NETWORKS 审中-公开

    公开(公告)号：US20190260778A1

    公开(公告)日：2019-08-22

    申请号：US16246774

    申请日：2019-01-14

    Applicant: NEC Laboratories America, Inc.

    Inventor： Haifeng Chen ,  Bo Zong ,  Christian Lumezanu

    IPC: H04L29/06 ,  H04W12/12 ,  G06N20/00

    Abstract: A method for detecting spoofing attacks from network traffic log data is presented. The method includes training a spoofing attack detector with the network traffic log data received from one or more mobile networks by extracting features that are relevant to spoofing attacks for training data, building a first set of vector representations for the network traffic log data, training an anomaly detection model by employing DAGMM, and obtaining learned parameters of DAGMM. The method includes testing the spoofing attack detector with the network traffic log data received from the one or more mobile networks by extracting features that are relevant to spoofing attacks for testing data, building a second set of vector representations for the network traffic log data, obtaining latent representations of the testing data, computing a z-score of the testing data, and creating a spoofing attack alert report listing traffic logs generating z-scores exceeding a predetermined threshold.

Patent Agency Ranking