A Novel Deep Learning Based Intrusion Detection System : Software Defined Network
Jamal Hussain, Vanlalruata Hnamte
Abstract
Over the last few years, Software Defined Networking (SDN) has brought forward a potential software-based networking framework that, with the existing network management system, allows for the network programming to operate alongside the overall network management system. Tracking data to the data centre is more effective with this new method. It prevents security flaws from causing new threats to appear in the network since these vulnerabilities will only reveal themselves at the time of OpenFlow packet transmission via a centralized system and symmetric controller. A study was conducted for a Deep Learning (DL) based approach that is proposed to be implemented on SDN. The Deep Neural Network model is used to monitor network activity for both regular and anomalous data transfer to check for malicious traffic. A dataset of IDS, publicly accessible, KDD-CUP99, NSL-KDD and UNSW-NB15 Dataset are used to determine the possible behaviour of security flaws. The study explores SDN security and IDS about security concerns and also gives a very high and acceptable accuracy rate.