A detection method of ddos ​​attack in sdn network based on optimized bp neural network

Abstract

The invention proposes a DDoS attack detection method for SDN network based on optimized BP neural network, which comprehensively extracts six characteristic values ​​related to DDoS in the flow table in the SDN network environment, source IP address growth rate GSIP, flow survival time change ADF , convection ratio PPF, port growth rate GSP, flow entry rate RFE, flow matching success rate RFM; reduce the load of SDN network by setting trigger threshold, optimize BP neural network with particle swarm algorithm, and use the characteristics of particle swarm optimization global optimization , select the mean square error of the BP neural network as the fitness function of the particle swarm algorithm, and select the best fitness value as the threshold and weight of the BP neural network, so as to avoid the slow convergence speed of the BP neural network when it solves the optimal solution and fall into Local optimal solution, and improve detection accuracy.

Description

technical field [0001] The invention relates to a DDoS attack detection technology, in particular to an SDN network DDoS attack detection method based on an optimized BP neural network. Background technique [0002] Distributed Denial of Service (DDoS) attacks are one of the main threats facing the current Internet. DDoS attack initiators first exploit the vulnerabilities of Internet users to collect a large number of puppet machines, and then coordinately schedule these puppet machines to forge data and send illegal requests to paralyze the target host. This resulted in the leakage of personal information of a large number of users and brought economic losses to many enterprises. The existing DDoS attack detection is mainly aimed at the traditional network architecture, while the software-defined network is a new type of network architecture that has emerged. certain advantages. However, because the control plane and data plane are decoupled, when the connection between ...

AI Technical Summary

Problems solved by technology

However, because the control plane and the data plane are decoupled, when the connection between the switch and the controller fails, the network will lose control. Therefore, the security of the controller is one of the security guarantees of the entire SDN network, and DDoS attacks are the security of the controller. One of the threats, how to detect accurate DDoS attacks is critical to SDN security

[0003] In the existing methods, although a large number of effective DDoS attack detection methods are proposed, they only use the interactive characteristics of the network flow, and the comprehensiveness of the judgment is not enough, or the convergence speed of the detection model is slow, the training time is long, and the algorithm used has not been optimized. Excellent solution, low accuracy of detection results

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