OptiML: An Enhanced ML Approach Towards Design of SDN based UAV Networks

Abstract

Unmanned Aerial Vehicles (UAV) network has been explored widely in recent years. The deployment of small to medium-scale UAVs are being considered for several real-time applications such as monitoring, surveillance, search and rescue services. Throughput in the communication model plays a vital role in improvising available capacity and balancing the data traffic loads. Often UAVs carry real-time sensitive data which are vulnerable to cyber-attacks and the UAVs’ wireless communication networks become more vulnerable to various forms of cyber-attacks. In this paper, we propose a secure machine learning-based approach to maximize the throughput of a Software Defined Network (SDN) controller for better UAV communications and security. The proposed approach consist of three steps, a) optimal placement and user association of UAVs using Genetic algorithm b) SDN controller placement using shortest path via single connected graph and c) creation and detection of DDoS attack using Feedforward neural network classifier. The best position of UAVs as base stations is obtained from the energy’s estimation of previous base station and users. After UAVs optimal placement, the availability of network capacity under SDN controller is done by finding the shortest path using bellman ford algorithm. It intends to balance the loads and generate traffic-free data transmission. Additionally, detection of DDoS attacks is also studied. The combination of two feature reduction techniques, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are employed to find the features relevant to DDoS attacks. These features are then fed into the Feedforward neural network classifier that classifies the normal and abnormal network traffic data of UAVs. The proposed approach is evaluated using simulations. Experimental results show high efficiency and security measures in SDN-based UAV networks.