Network intrusion detection in software defined networking with self-organized constraint-based intelligent learning framework

Abstract

With the advent of internet and communication system, a huge number of opportunities have been presented to humans, however, its vision will not be easy and comfortable. Instead the current network systems are filled up with slew of problems that must be addressed on timely basis. One of the prominent concerns in IoT is security. A number of security methods have been proposed but they are still in their lowest levels and needs upgrade. One such solution is Software defined network (SDN). Although SDN is anticipated to provide a favorable atmosphere for different security activities in IoT, there is still so much progress to be made because SDN cannot solve security challenges on its own. Furthermore, the SDN inherently imposes additional security flaws. Because of the large susceptibility area in SDN-based IoT systems, a wide range of faults like DOS, DDOS, U2R etc., are directed against them. In addition to this, majority of the current IDS were based on machine learning techniques, however, the problem with such methods was that they generated high false rates and were not producing effective results when datasets were non-linear. Moreover, overfitting caused by the noisy data also hindered the performance of ML based systems. Therefore, the objective is to review and provide an effective and efficient intrusion detection techniques that solves the issues addressed.

Keywords

• Intrusion detection system
• Software defined network
• Distributed denial of service attack
• Denial of service attack
• Internet of things
• Security, Network infrastructure