A Data Analysis Based Approach for Distributed Intrusion Detection System

Abstract

Software Defined Network has brought the encouraging methods of dealing with the complex network. Managing the network was not easy with traditional rules. The softwaredefined feature allows taking advantage of programmability and scalability. The major distinction between SDN and the traditional methods of managing the network is the separation of control plan and data plan in SDN. The considerable issue with the networks is regarding security and SDN controller is the main focus of attackers for the attack. To prevent the network from attacks integrated Intrusion Detection System is used. Virtual testbed is used which works like a real environment. Star topology is employed by connection server and host with open flow switch. NIDS is deployed in the network to monitor the traffic on anomaly-based method. The detection Flow-based model is deployed using machine learning to improve the detection and overcome the limitations of signature-based IDS. Techniques works positively and shown the improvement in detecting the intrusion in network by using machine learning model.