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Abnormal flow detection method and system based on hybrid neural network

A hybrid neural network and abnormal traffic technology, applied in the field of abnormal traffic detection methods and systems, can solve the problems of high false alarm rate, inability to effectively learn high-dimensional features, low intrusion detection accuracy, etc., and achieve the best detection rate and detection accuracy. , strong practicability and high efficiency

Inactive Publication Date: 2019-08-16
FUZHOU UNIV
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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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  • Abnormal flow detection method and system based on hybrid neural network
  • Abnormal flow detection method and system based on hybrid neural network
  • Abnormal flow detection method and system based on hybrid neural network

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Embodiment Construction

[0030] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0031] It should be pointed out that the following detailed description is exemplary and is intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0032] It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combina...

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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 ...

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Application Information

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IPC IPC(8): H04L29/06G06N3/08G06N3/04
CPCH04L63/1425H04L63/1416G06N3/08G06N3/044G06N3/045
Inventor 郭文忠连鸿飞张浩谢麟
Owner FUZHOU UNIV
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