Traffic anomaly detection method, model training method and device

A traffic anomaly and model-determining technology, applied in digital transmission systems, data exchange networks, electrical components, etc., can solve the problem of low accuracy of network traffic data, incompetence of manual work for abnormal detection, and network traffic data distribution that does not obey the normal distribution. and other problems to achieve the effect of improving the accuracy
CN110266552AActive Publication Date: 2019-09-20HUAWEI TECH CO LTD
9 Cites 13 Cited by

Patent Information

Authority / Receiving Office
CN Ā· China
Patent Type
Applications(China)
Current Assignee / Owner
HUAWEI TECH CO LTD
Publication Date
2019-09-20

Smart Images

  • Figure 1
    Figure 1
  • Figure 2
    Figure 2
  • Figure 3
    Figure 3
Patent Text Reader

Abstract

The invention provides a traffic abnormality detection method. The method comprises the steps of obtaining a target time sequence comprising N elements; according to the target time sequence, obtaining target parameters of the target time sequence, wherein the target parameters comprise a periodic factor and / or jitter density, the periodic factor represents one type of waveform change which is presented in the target time sequence and surrounds the long-term trend, and the jitter density represents the deviation of the actual value and the target value of the target time sequence in the target time; determining a first type to which the target time sequence belongs from a plurality of types according to the target parameter, each type in the plurality of types corresponding to a parameter set, and the target parameter belonging to the parameter set corresponding to the first type; and according to the first type of judgment model corresponding to the first type, the abnormal condition of the target time sequence is detected, and each type in the multiple types corresponds to one type of judgment model. According to the technical scheme, the accuracy of flow abnormity detection can be improved.
Need to check novelty before this filing date? Find Prior Art

Description

technical field

[0001] The present application relates to the field of machine learning, and more specifically, relates to a traffic anomaly detection method, model training method and device. Background technique

[0002] In the field of machine learning, anomaly detection refers to the detection of models, data or events that do not conform to predictions. Usually anomaly detection is learned by professionals on historical data, and then finds outliers. Data sources include applications, processes, operating systems, devices, or networks. With the increase in the complexity of computing systems, humans are no longer competent for the current difficulty of anomaly detection.

[0003] In the prior art, an algorithm based on statistics and data distribution is used to detect anomalies in network traffic data. The premise is that the traffic data obeys a normal distribution in a short period of time. However, the distribution of network traffic data does not obey the normal d...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More