Intrusion Detection System for Software-Defined Networks Using Fuzzy System

Abstract

In the field of computer networks, one among the latest technologies which is attracting researchers is software-defined networking (SDN). The network is centrally managed through software-based SDN controllers. SDN offers several advantages over conventional networks. Our area of interest is to enhance the security of present networks to a greater extent through centralized SDN controllers. Mechanisms to achieve this goal via SDN should be devised. An efficient intrusion detection system which is capable of monitoring real-time network traffic and reporting about intrusion if any to the controller is a good solution to this problem. Intrusion detection system (IDS) exists for traditional networks. Introducing IDS in SDN field results in achieving better efficiency compared to traditional networks, since it allows the controller to take immediate action on the attacker as soon as the attack is found. The aim of this project is to enhance security in networks through SDN by developing IDS using machine learning technique, namely fuzzy approach. The advantage of using this approach is that it provides high attack detection rate and less false alarm rate.