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Ddos attack detection method and device based on multi-scale convolutional neural network

A convolutional neural network and attack detection technology, applied in the Internet field, can solve the problems of low detection rate, false positive rate and high error rate, and achieve the effect of improving detection rate, reducing false positive rate and error rate, and improving computing efficiency

Active Publication Date: 2021-07-13
HAINAN UNIV
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Problems solved by technology

[0005] In the big data environment, the existing DDoS attack detection methods have the problems of low detection rate, high false positive rate and high error rate.

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  • Ddos attack detection method and device based on multi-scale convolutional neural network
  • Ddos attack detection method and device based on multi-scale convolutional neural network
  • Ddos attack detection method and device based on multi-scale convolutional neural network

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Embodiment Construction

[0058] In order to make the object, technical solution and advantages of the present invention clearer, the implementation manner of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0059] see figure 1 , the embodiment of the present application provides a DDoS attack detection method based on a multi-scale convolutional neural network, and the method includes the following steps.

[0060] Step 101: Obtain network traffic, and define feature extraction rules for the network traffic.

[0061]In implementation, firstly, the network traffic of the victim host can be obtained, wherein the victim host can be any server or personal computer. Afterwards, the network traffic extraction rules can be defined according to the difference in characteristics between normal network traffic and attacked network traffic. It is worth mentioning that the difference in characteristics between normal network traffic and attacked net...

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Abstract

The invention discloses a DDoS attack detection method and device based on a multi-scale convolutional neural network, belonging to the technical field of the Internet. The method includes: acquiring network traffic, defining feature extraction rules of the network traffic; extracting grayscale matrix features of the network traffic through the feature extraction rules; establishing multi-scale convolution according to the grayscale matrix features A neural network model; based on the multi-scale convolutional neural network model, detecting the DDoS attack of the network traffic. By adopting the invention, the detection rate of DDoS attack detection can be improved.

Description

technical field [0001] The invention relates to the technical field of the Internet, in particular to a DDoS attack detection method and device based on a multi-scale convolutional neural network. Background technique [0002] Distributed Denial of Service attack (Distributed Denial of Service, DDoS) originates from Denial of Service attack (Denial of Service, DOS), is an attack method commonly used by hackers, and it is difficult to prevent. Usually initiated by an organized, distributed or remotely controlled botnet. DDoS attacks usually manifest as interruptions or delays in network connections, which will degrade network performance and cause network paralysis. [0003] Existing DDoS attack detection methods mainly include statistics-based DDoS attack detection methods and machine learning-based DDoS attack detection methods. The DDoS attack detection method based on statistics is realized by analyzing the law of statistical eigenvalues ​​of a large amount of data. It...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L29/06H04L12/24
CPCH04L41/142H04L41/145H04L63/1416H04L63/1425H04L63/1458
Inventor 程杰仁唐湘滟黄梦醒刘译夫李梦洋李俊麒
Owner HAINAN UNIV