Internet traffic classification using machine learning approach: Datasets validation issues

Ibrahim, Hamza Awad Hamza; Zuobi, Omer Radhi Aqeel Al; Al-Namari, Marwan A.; MohamedAli, Gaafer; Abdalla, Ali

doi:10.1109/sgcac.2016.7458022

Cited by 17 publications

(5 citation statements)

References 31 publications

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“…As a result, several source applications can be recognized. The approach can classify flows into clusters with similar patterns by detecting trends in their properties such as the size of the initial few packets, arrival timings, packet length, IP address, round trip time, and source/destination ports [55,56].…”

Section: Statistical-based Tcmentioning

confidence: 99%

Machine-Learning-Based Traffic Classification in Software-Defined Networks

Serag,

Abdalzaher,

Elsayed

et al. 2024

Electronics

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Many research efforts have gone into upgrading antiquated communication network infrastructures with better ones to support contemporary services and applications. Smart networks can adapt to new technologies and traffic trends on their own. Software-defined networking (SDN) separates the control plane from the data plane and runs programs in one place, changing network management. New technologies like SDN and machine learning (ML) could improve network performance and QoS. This paper presents a comprehensive research study on integrating SDN with ML to improve network performance and quality-of-service (QoS). The study primarily investigates ML classification methods, highlighting their significance in the context of traffic classification (TC). Additionally, traditional methods are discussed to clarify the ML outperformance observed throughout our investigation, underscoring the superiority of ML algorithms in SDN TC. The study describes how labeled traffic data can be used to train ML models for appropriately classifying SDN TC flows. It examines the pros and downsides of dynamic and adaptive TC using ML algorithms. The research also examines how ML may improve SDN security. It explores using ML for anomaly detection, intrusion detection, and attack mitigation in SDN networks, stressing the proactive threat-detection and response benefits. Finally, we discuss the SDN-ML QoS integration problems and research gaps. Furthermore, scalability and performance issues in large-scale SDN implementations are identified as potential issues and areas for additional research.

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Section: Statistical-based Tcmentioning

confidence: 99%

Machine-Learning-Based Traffic Classification in Software-Defined Networks

Serag,

Abdalzaher,

Elsayed

et al. 2024

Electronics

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“…KNN is one of the most fundamental and simplest in the supervised machine learning algorithms, which operates by grouping all the samples having similar characteristics of the dataset [21]. Instead of learning from the training data, KNN mechanically memorizes all the data.…”

Section: ) K-nearest Neighbors (Knn)mentioning

confidence: 99%

A Comparative Study of Machine Learning-based Approach for Network Traffic Classification

Trang

Nguyen

2022

Knowl. Eng. Data Sci.

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Internet usage has increased rapidly and become an essential part of human life, corresponding to the rapid development of network infrastructure in recent years. Thus, protecting users’ confidential information when joining the global network becomes one of the most significant considerations. Even though multiple encryption algorithms and techniques have been applied in different parties, including internet providers, and web hosting, this situation also allows the hacker to attack the network system anonymously. Therefore, the significance of classifying network data streams to improve network system quality and security is attracting increasing study interests. This work introduces a machine learning-based approach to find the most suitable training model for network traffic classification tasks. Data pre-processing is first applied to normalize each feature type in the dataset. Different machine learning techniques, including k-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Random Forest (RF), are applied based on the normalized features in the classification phase. An open-access dataset ISCXVPN2016 is applied for this research, which includes two types of encryption (VPN and Non-VPN) and seven classes of traffic categories classes. Experimental results on the open dataset have shown that the proposed models have reached a high classification rate – over 85% in some cases, in which the RF model obtains the most refined results among the three techniques.

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“…Machine Learning based techniques are implemented to classify traffic flows without requiring deep inspection of packet contents [14]. Without regard to the customised algorithms, researchers are looking at these techniques as the best substitute because they have a much lower computational cost and are able to detect encrypted traffic [15].…”

Section: Streamingmentioning

confidence: 99%

Deep Learning Approach for Real-time Video Streaming Traffic Classification

Jameel

Turner

Kanakis

et al. 2022

2022 International Conference on Computer Science and Software Engineering (CSASE)

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Video streaming services such as Amazon Prime Video, Netflix and YouTube, continue to be of enormous demands in everyday peoples' lives. This enticed research in new mechanisms to provide a clear image of network usage and ensure better Quality of Service (QoS) for these applications. This paper proposes an accurate video streaming traffic classification model based on deep learning (DL). We first collected a set of video traffic data from a real network. Then, data was pre-processed to select the desired features for video traffic classification. Based on the performance evaluation, the model produces an overall accuracy of 99.3% when classifying video streaming traffic using a multi-layer feedforward neural network. This paper also evaluates the DL approach's effectiveness compared to the Gaussian Naive Bayes algorithm (GNB), one of the most well-known machine learning techniques used in Internet traffic classification. The model is promising to be applied in a real-time scenario as it showed its ability to predict new unseen data with 98.4% overall accuracy.

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Internet traffic classification using machine learning approach: Datasets validation issues

Cited by 17 publications

References 31 publications

Machine-Learning-Based Traffic Classification in Software-Defined Networks

Machine-Learning-Based Traffic Classification in Software-Defined Networks

A Comparative Study of Machine Learning-based Approach for Network Traffic Classification

Deep Learning Approach for Real-time Video Streaming Traffic Classification

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