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.

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.