Research on deep learning-based malicious traffic detection and defense mechanism

In order to solve the problem of low accuracy of malicious traffic detection and defense in SDN network, the research of malicious traffic detection and defense mechanism based on deep learning is proposed. Based on the idea of network monitoring, this paper proposes a malicious traffic detection and defense method for SDN network, which improves the accuracy and efficiency of malicious traffic detection by monitoring the link traffic and using the improved convolutional neural network model for malicious traffic identification, and can detect and defend the malicious traffic in the network. Experimental results show that the overall accuracy of malicious traffic detection using neural network can reach 96.7%, and the identification of each type of malicious traffic has achieved good results, which is a good foundation for the next step of malicious traffic defense. Conclusion: The defense is carried out by generating flow rules. The proposed method is tested in the hardware environment, and the results show that it can effectively detect malicious traffic and switches and take defense measures to improve the security of the network.