论文标题
使用深度学习的新型网络入侵检测系统的新方法：未来派方法
A Novel Approach To Network Intrusion Detection System Using Deep Learning For Sdn: Futuristic Approach
论文作者
论文摘要
软件定义的网络（SDN）是改变传统网络架构的下一代。 SDN是更改Internet网络架构的有希望的解决方案之一。由于SDN体系结构的集中性质，攻击变得更加普遍。为SDN提供安全性至关重要。在这项研究中，我们在SDN的背景下提出了网络入侵检测系统深度学习模块（NIDS-DL）方法。我们建议的方法将网络入侵检测系统（NID）与多种类型的深度学习算法相结合。我们的方法使用功能选择方法采用了从NSL-KDD数据集中41个功能提取的12个功能。我们使用分类器（CNN，DNN，RNN，LSTM和GRU）。当我们比较分类器得分时，我们的技术产生的准确性结果为（98.63％，98.53％，98.13％，98.04％和97.78％）。我们的新方法的新颖性（NIDS-DL）使用了5个深度学习分类器，并制作预处理数据集以收获最佳结果。我们提出的方法在二元分类和检测攻击方面取得了成功，这意味着我们的方法（NIDS-DL）将来可能会以很高的效率使用。
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.
