Abnormal flow detection method and system based on hybrid neural network

Abstract

The invention relates to an abnormal flow detection method and system based on a hybrid neural network, and the method comprises the steps: firstly, collecting network flow data, and carrying out thefeature extraction and data preprocessing through taking network flow as granularity; learning spatial features in the network traffic data through a convolutional neural network; inputting the features containing the spatial information into a bidirectional long-short time memory network to further learn the time sequence features of the bidirectional long-short time memory network; finally, outputting a detection result. Compared with an existing machine learning and deep learning abnormal flow detection method, the method has the advantages that high-dimensional features can be better mined, and the accuracy of an intrusion detection model is improved. The method is reasonable in design, and the accuracy rate, the detection rate and the accuracy rate of the obtained classification modelare all high.

Description

technical field [0001] The invention relates to the technical field of computer network security, in particular to a hybrid neural network-based abnormal flow detection method and system. Background technique [0002] As an effective method to realize network intrusion detection, network traffic anomaly detection can not only detect unknown network attacks, but also provide important support for network situational awareness. Abnormal network traffic has a great impact on the availability of the network, and even causes users to be unable to access the Internet normally. The main causes of abnormal network traffic are: first, network performance reasons, mainly referring to abnormal traffic caused by unreasonable network topology design or improper user operation, such as improper network policy settings by network administrators, network equipment failures, etc.; second, network security Reasons mainly refer to abnormal traffic caused by malicious network attacks, such as ...

AI Technical Summary

Problems solved by technology

The high-dimensional features in network traffic data cannot be effectively learned, and there are problems of low intrusion detection accuracy and high false alarm rate

Existing intrusion detection deep learning models only use some features in network traffic data, which has certain limitations

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