DDoS attack detection system and method based on back-propagation neural network algorithm

Abstract

A DDoS attack detection system based on a back-propagation neural network algorithm comprises a data acquisition module, a data preprocessing module, a resource scheduling module, a rule base module,data analysis modules and a response module, wherein the data acquisition module is used to collect network traffics; the data preprocessing module is used to preprocess original network traffic data;the resource scheduling module is used to allocate the appropriate data analysis module for data analysis; the rule base module is used to filter the data with intrusion features; the data analysis modules are used to real-timely update rules of the rule base module; and the response module is used to detect intrusion behaviors and give responses and alarms. The DDoS attack detection system disclosed by the invention has the beneficial effects as follows: the accuracy of attack detection can be improved; the situation that a traditional detection method based on a feature detection algorithmcan only detect known attack modes and can helplessly process unknown attacks can be improved; and the shortcomings of slow convergence speed and low accuracy of a traditional back-propagation neuralnetwork algorithm can be improved.

Description

technical field [0001] The invention relates to the technical field of network security, in particular to a DDoS attack detection system and method based on a backpropagation neural network algorithm. Background technique [0002] Under the strong impetus of network technology, the Internet network has been developed unprecedentedly and has brought about major changes in human life style. Today, the application of network technology has penetrated into all aspects of social life such as economy, culture, military affairs, and politics. Thousands of companies, enterprises, and individuals are surfing the Internet every day, and Internet users are increasing rapidly. As people rely more and more on the Internet, network security issues have become increasingly prominent. For economic, political, military, revenge and other purposes, attackers use the loopholes of computers and networks to launch various attacks on target machines, causing great harm to network security. DDoS...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the deficiencies of the prior art, the present invention provides a DDoS attack detection system and method based on the backpropagation neural network algorithm. By using the dynamic rule base, the traditional feature-based detection algorithm can only be used for known attack patterns. The detection power is powerless to unknown attacks; the present invention uses the improved backpropagation neural network algorithm to improve the shortcomings of the traditional backpropagation neural network algorithm, such as slow convergence speed and low precision.

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