ENHANCED ENERGY EFFICIENCY AND POWER ANALYSIS IN SOFTWARE-DEFINED NETWORKS THROUGH MALICIOUS SWITCH DETECTION

Software Defined Networking (SDN) has progressed by a network model to assist with different requirement of a real time traffic flow in a huge scale switch. The basic function of SDN plays a vital role in partition of data plane from the controller plane with accepting the needed changes as per the necessities. SDN provides the broad programmability of switches and its architecture concepts for the services and applications. In our proposed system, deep learning atmosphere is generated using TensorFlow and Keras atmosphere. It involves in Kaggle dataset for the trusted and the malicious flow detection. Through analysis part, power analysis is added in the controller built. SDN overall performance depends on energy consumption, load balancing and traffic management. In this proposed approach energy efficiency and badge power of the Software Defined Network is presented. We focused on the energy efficiency of the network by improving the already proposed route selection algorithm. We proposed a method for the analysis to determine the power management functionalities in various domains. The stimulation results show the power analysis feature classification helps to identify the trusted flow of switches. We propose an empirical algorithm and mathematical model of energy consummation determines controller plane to maximize the network performance and scalability. The overall performance study determines that the proposed system of drop rate, constant throughput, load balancing and energy efficiency of switches in SDN is better than the already existing algorithms. The comparative study determines the proposed algorithm is approximately saves 30% of energy.