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

arXiv: https://arxiv.org/abs/2208.02094 · HTML: https://mlsanity.com/paper/2208.02094/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 18 references that are in the map.
- First posted on arXiv 3 Aug 2022.

## Built on by (most cited first, 2 of 2)

- [Analisis Eksploratif Dan Augmentasi Data NSL-KDD Menggunakan Deep Generative Adversarial Networks Untuk Meningkatkan Performa Algoritma Extreme Gradient Boosting Dalam Klasifikasi Jenis Serangan Siber](https://mlsanity.com/paper/2312.10669/) (2023)
- [Sdn Intrusion Detection Using Machine Learning Method](https://mlsanity.com/paper/2411.05888/) (2024)

## Abstract (arXiv)

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.