INTRODUCING A NEW INTRUSION DETECTION METHOD IN THE SDN NETWORK TO INCREASE SECURITY USING DECISION TREE AND NEURAL NETWORK

Abstract

Computer networks are difficult to use due to the large number of devices such as router, switch, hop, and many sophisticated security management protocols, but in networks defined with integrated management and configuration software, software-based networks are nowadays important. They are high-end and will become one of the most used and important communication tools in the IT world in the future. In these networks, like all other networks, data security and protection is crucial because a network that is not secure will not work, in this paper, we present a new method of intrusion detection in this network, which consists of two parts: training and testing. Looking to determine if the network is normal or not? By checking the output of these two categories, the current status of the network is determined. The proposed method uses decision tree and neural network. In each first class of tree, the classification of abnormal data is classified and in the second class, the norm data is in decision tree. The output of the decision tree is neural network input Shows that the proposed method performs well.