Training method and detection method of flow detection model of asymmetric convolutional network

Abstract

The invention discloses a training method and a detection method of a flow detection model of an asymmetric convolutional network. The flow detection model of the asymmetric convolutional network comprises an asymmetric convolutional self-encoding network and a classification network. The training method comprises the steps: constructing the symmetric convolutional self-encoding network, wherein the symmetric convolutional self-encoding network comprises an encoding network and a decoding network; training the symmetric convolutional self-encoding network by using a training sample; removing adecoding network in the trained symmetric convolutional self-encoding network to obtain an asymmetric convolutional self-encoding network; and extracting abstract features of a training sample by using the asymmetric convolutional self-encoding network, and training a classification network by using the abstract features so as to complete training of a flow detection model of the asymmetric convolutional network. Compared with the existing detection model, the method has higher detection accuracy and lower false alarm rate, and the detection model only reserves the coding network, so that themodel is lighter and easier for feature extraction, and the overhead is saved.

Description

technical field [0001] The invention belongs to the field of information technology, and in particular, relates to a training method and a detection method of a traffic detection model of an asymmetric convolution network, a computer-readable storage medium, and a computer device. Background technique [0002] With the rapid development of the Internet and the continuous expansion of the network scale, the Internet has become an indispensable part of human production and life. But at the same time, people inevitably suffer from network anomalies in the process of enjoying the convenience of the network. A variety of network anomalies that are prevalent at present can be manifested through abnormal network traffic. Abnormal network traffic can more comprehensively reflect the real-time status of the network, such as network scanning, DDoS attacks, network worms, etc., to discover the network in time Abnormal traffic changes are of great significance to the abnormal location ...

AI Technical Summary

Problems solved by technology

[0005] When traditional machine learning is applied to the anomaly detection of the model, there is a problem of prior experience, especially for network traffic data with a large amount of data and strong real-time performance. Inappropriate parameter selection or poor quality of selected data will affect the The detection effect of the model has a relatively large impact

Existing detection models have low accuracy and long detection time for network traffic anomaly detection

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