The advancement of cloud and IoT technologies, has made network administration more difficult. Software-Defined Networking is one of the trending technologies which replaces the traditional networking domain with the programmable network configuration. In the current development of the network architecture, data security plays a prominent role. Many strategies for dealing with network attacks have been developed, among them deep learning is one of the most advanced technology. The paper aims to classify the network traffic into normal traffic and attack traffic with Multilayer Perceptron (MLP). The simulation uses a python programming language with many packages like Numpy, sci-kit, seaborn, etc. in a mininet SDN test bed with the Ryu controller. From the obtained results proposed algorithm gives better accuracy for classifying the attack traffic and normal traffic in the network.