A Novel Approach To Network Intrusion Detection System Using Deep Learning For Sdn: Futuristic Approach

TL;DR

This paper introduces a deep learning-based Network Intrusion Detection System tailored for Software-Defined Networking, achieving high accuracy in attack detection by combining feature selection and multiple classifiers.

Contribution

It presents a novel NIDS-DL approach that integrates five deep learning classifiers with feature selection for SDN security, demonstrating improved detection performance.

Findings

Achieved over 98% accuracy with various classifiers

Effective feature selection from NSL-KDD dataset

Successful binary attack classification

Abstract

Software-Defined Networking (SDN) is the next generation to change the architecture of traditional networks. SDN is one of the promising solutions to change the architecture of internet networks. Attacks become more common due to the centralized nature of SDN architecture. It is vital to provide security for the SDN. In this study, we propose a Network Intrusion Detection System-Deep Learning module (NIDS-DL) approach in the context of SDN. Our suggested method combines Network Intrusion Detection Systems (NIDS) with many types of deep learning algorithms. Our approach employs 12 features extracted from 41 features in the NSL-KDD dataset using a feature selection method. We employed classifiers (CNN, DNN, RNN, LSTM, and GRU). When we compare classifier scores, our technique produced accuracy results of (98.63%, 98.53%, 98.13%, 98.04%, and 97.78%) respectively. The novelty of our new approach (NIDS-DL) uses 5 deep learning classifiers and made pre-processing dataset to harvests the best results. Our proposed approach was successful in binary classification and detecting attacks, implying that our approach (NIDS-DL) might be used with great efficiency in the future.