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1.发明申请公开(公告)号：US20220255817A1
公开(公告)日：2022-08-11
申请号：US17480070
申请日：2021-09-20
Inventor： Won Ki HONG , Jae Hyoung YOO , Ji Bum HONG , Su Hyun PARK
Abstract: A virtual network management-specific machine learning-based VNF anomaly detection system may comprise: a data collection unit configured to collect normal state data generated when a service is normally provided and abnormal state data generated through a fault injection method through a monitoring agent and a monitoring module in real time, store the collected data in a time-series database, and transmit the monitoring data to determine whether there is an abnormal state; and a data analysis unit configured to extract a feature necessary for detecting an abnormal state by pre-processing monitoring data received from the data collection unit and send data on the extracted data to an abnormal-state detection model so that the abnormal-state detection model analyzes data that is input in real time to determine whether there is an abnormal state and notifies a network manager when an abnormal state occurs.
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2.发明申请公开(公告)号：US20200167610A1
公开(公告)日：2020-05-28
申请号：US16691505
申请日：2019-11-21
Inventor： Won Ki HONG , Jae Hyoung YOO , Do Young LEE , Hee Gon KIM
Abstract: The present invention relates to a technique in which demand prediction of resources of virtual network functions (VNFs) that provide a core technology in a network virtualization environment is performed using machine learning technology. In the present invention, in order to predict VNF resource information, not only are the resources of the VNFs as data but also information of surrounding VNFs that are directly or indirectly related are used, and prediction is possible even in a dynamically changed network environment. In addition, service function chain (SFC) data among various pieces of network information is used to reduce a time required for machine learning according to a size of an entire network.
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公开(公告)号：US20240236007A9
公开(公告)日：2024-07-11
申请号：US17768837
申请日：2020-04-10
Inventor： Won Ki HONG , Jae Hyoung YOO , Ji Bum HONG
IPC: H04L47/2441 , G06N20/20 , H04L41/16 , H04L43/026
CPC classification number: H04L47/2441 , G06N20/20 , H04L41/16 , H04L43/026
Abstract: A traffic categorization method and device are disclosed. A traffic categorization method according to one embodiment of the present invention may comprise the steps of: receiving flow data comprising information about a flow; scaling for the flow data; generating input data by removing, on the basis of a correlation, overlapping data from the scaled flow data; and categorizing a network traffic on the basis of the input data.
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公开(公告)号：US20240137323A1
公开(公告)日：2024-04-25
申请号：US17768837
申请日：2020-04-09
Inventor： Won Ki HONG , Jae Hyoung YOO , Ji Bum HONG
IPC: H04L47/2441 , G06N20/20 , H04L41/16 , H04L43/026
CPC classification number: H04L47/2441 , G06N20/20 , H04L41/16 , H04L43/026
Abstract: A traffic categorization method and device are disclosed. A traffic categorization method according to one embodiment of the present invention may comprise the steps of: receiving flow data comprising information about a flow; scaling for the flow data; generating input data by removing, on the basis of a correlation, overlapping data from the scaled flow data; and categorizing a network traffic on the basis of the input data.
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