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    Automatic Detection and Mitigation of Denial-of-Service Attacks
    1.
    发明申请
    Automatic Detection and Mitigation of Denial-of-Service Attacks 有权

    公开(公告)号：US20220191242A1

    公开(公告)日：2022-06-16

    申请号：US17120050

    申请日：2020-12-11

    Applicant: Google LLC

    Inventor： Francois Pepin ,  Andre Lloyd Perlee Harder ,  Prajakta Joshi ,  Amitabha Roy ,  Saila Talagadadeevi ,  Emil Kiner ,  Chia- Tung Kuo ,  Jiayu Ye

    IPC: H04L29/06 ,  H04L12/24

    Abstract: A method for mitigating network abuse includes obtaining a first set of network traffic messages of network traffic currently received by a network service and determining, via a first model, whether network abuse is occurring based on the first set of network traffic messages. When the network abuse is occurring, the method includes obtaining a second set of current network traffic messages. The method also includes, for each network traffic message in the second set of network traffic messages, labeling, via a second model, the network traffic message as an abusing network traffic message or a non-abusing network traffic message. The method also includes generating, via a third model, at least one network traffic rule. Each network traffic rule, when implemented, reduces an effect of the abusing network traffic messages.

    HYBRID MESSAGE PASSING NEURAL NETWORK AND PERSONALIZED PAGE RANKING GRAPH CONVOLUTION NETWORK MODEL
    3.
    发明公开
    HYBRID MESSAGE PASSING NEURAL NETWORK AND PERSONALIZED PAGE RANKING GRAPH CONVOLUTION NETWORK MODEL 审中-公开

    公开(公告)号：US20240250958A1

    公开(公告)日：2024-07-25

    申请号：US18014287

    申请日：2021-12-27

    Applicant: GOOGLE LLC

    Inventor： Amitabha Roy ,  Nasrin Baratalipour ,  Vinit Jogani ,  Pushkarini Agharkar

    IPC: H04L9/40 ,  G06N3/0464

    CPC classification number: H04L63/14 ,  G06N3/0464

    Abstract: A method and system for classifying assets by features of individual entities and relations of the individual entities to the assets using a neural network is disclosed herein. The method comprises aggregating, at each of a plurality of aggregator nodes, data regarding features of each node in each distant neighborhood of a multiplicity of distant neighborhoods; updating, at each of the plurality of aggregator nodes, a state of the aggregator node by assigning a weight to each of the features of the corresponding distant neighborhood; updating, at the seed node, a state of the seed node by performing convolutional analysis of each node in a local neighborhood surrounding the seed node; and determining a label of the seed node based on the state of the seed node.

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