Biblio

with the development of SDN, ICN and 5G networks, the research of future network becomes a hot topic. Based on the design idea of SDN network, this paper analyzes the propagation model and detection method of malicious code in future network. We select characteristics of SDN and analyze the features use different feature selection methods and sort the features. After comparison the influence of running time by different classification algorithm of different feature selection, we analyze the choice of reduction dimension m, and find out the different types of malicious code corresponding to the optimal feature subset and matching classification method, designed for malware detection system. We analyze the node migration rate of malware in mobile network and its effect on the outbreak of the time. In this way, it can provide reference for the management strategy of the switch node or the host node by future network controller.

In practice, Defenders need a more efficient network detection approach which has the advantages of quick-responding learning capability of new network behavioural features for network intrusion detection purpose. In many applications the capability of Deep Learning techniques has been confirmed to outperform classic approaches. Accordingly, this study focused on network intrusion detection using convolutional neural networks (CNNs) based on LeNet-5 to classify the network threats. The experiment results show that the prediction accuracy of intrusion detection goes up to 99.65% with samples more than 10,000. The overall accuracy rate is 97.53%.