基于改进双向TCN模型的SDN异常流量检测OA

The centralized control feature of software defined network(SDN)technology enhances the efficiency of network management while also bringing more severe security threats.Accurately detecting abnormal traffic in the SDN network is critical for network security.To address the vulnerabilities of SDN networks to various attacks and the insufficient ability of existing methods in modeling the temporal characteristics of abnormal traffic,this paper proposes an abnormal traffic detection method suitable for the SDN environment.This method takes the five-tuple of the flow(source IP address,destination IP address,source port number,destination port number,transport layer protocol)as the division basis.The length sequence of data packets is extracted as the core temporal features.Based on the improved bidirectional temporal convolutional network(BiTCN),by changing the ELU activation function and adding a residual block in the original TCN structure,and simultaneously integrating the multi-head squeeze excitation mechanism(MSE)to enhance the feature modeling ability,the identification of abnormal behaviors is achieved.The experimental results show that the method proposed in this paper achieves good effects on the public SDN dataset,and its accuracy,precision and other indicators are superior to the traditional baseline models.