Salau Ayodeji Olalekan, Beyene Melesew Mossie
Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, Nigeria.
Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.
Sci Rep. 2024 Aug 29;14(1):20060. doi: 10.1038/s41598-024-70983-6.
The classification of network traffic has become increasingly crucial due to the rapid growth in the number of internet users. Conventional approaches, such as identifying traffic based on port numbers and payload inspection are becoming ineffective due to the dynamic and encrypted nature of modern network traffic. A number of researchers have implemented Software Defined Networking (SDN) based traffic classification using Machine Learning (ML) and Deep Learning (DL) models. However, the studies had various limitations such as encrypted traffic detection, payload inspection, poor detection accuracy, and challenges with testing models both in offline and real-time traffic modes. ML models together with SDN are adopted nowadays to enhance classification performance. In this paper, both supervised (Logistic Regression, Decision Tree, Random Forest, AdaBoost, and Support Vector Machine) and unsupervised (K-means clustering) ML models were used to classify Domain Name System (DNS), Telnet, Ping, and Voice traffic flows simulated using the Distributed Internet Traffic Generator (D-ITG) tool. The use of this tool effectively manages and classifies traffic types based on their application. The study discussed the dataset used, model selection, implementation of the model, and implementation techniques (such as pre-processing, feature extraction, ML algorithm, and model evaluation metrics). The proposed model in SDN was implemented in Mininet for designing the network architecture and generating network traffic. Anaconda Python environment was utilized for traffic classification using various ML techniques. Among the models tested, the Decision Tree supervised learning achieved the highest accuracy of 99.81%, outperforming other supervised and unsupervised learning algorithms. These results indicate that the integration of ML with SDN provides an efficient classification method for identifying and accurately classifying both offline and real-time network traffic, enhanced quality of service (QoS), detection of encrypted packets, deep packet inspection and management.
由于互联网用户数量的迅速增长，网络流量分类变得越来越重要。传统方法，如基于端口号和有效载荷检查来识别流量，由于现代网络流量的动态性和加密性，正变得越来越无效。许多研究人员已经使用机器学习（ML）和深度学习（DL）模型实现了基于软件定义网络（SDN）的流量分类。然而，这些研究存在各种局限性，如加密流量检测、有效载荷检查、检测准确率低，以及在离线和实时流量模式下测试模型时面临的挑战。如今，ML模型与SDN一起被采用以提高分类性能。在本文中，监督学习（逻辑回归、决策树、随机森林、自适应增强和支持向量机）和无监督学习（K均值聚类）ML模型都被用于对使用分布式互联网流量生成器（D-ITG）工具模拟的域名系统（DNS）、Telnet、Ping和语音流量流进行分类。该工具的使用有效地根据流量的应用对流量类型进行管理和分类。该研究讨论了所使用的数据集、模型选择、模型的实现以及实现技术（如预处理、特征提取、ML算法和模型评估指标）。SDN中提出的模型在Mininet中实现，用于设计网络架构和生成网络流量。利用Anaconda Python环境，使用各种ML技术进行流量分类。在测试的模型中，决策树监督学习达到了99.81%的最高准确率，优于其他监督学习和无监督学习算法。这些结果表明，ML与SDN的集成提供了一种有效的分类方法，用于识别和准确分类离线和实时网络流量、提高服务质量（QoS）、检测加密数据包、深度数据包检查和管理。
