Proceedings Article10.1109/ICTC49870.2020.9289174
Traffic Data Classification using Machine Learning Algorithms in SDN Networks
Jungmin Kwon,Daeun Jung,Hyunggon Park +2 more
- 21 Oct 2020
- pp 1031-1033
21
TL;DR: In this article, the authors study the automatic network data classification based on machine learning, where several machine learning algorithms are deployed to automatically classify real network traffic data collected from ONOS (Open Network Operating System) platform.
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Abstract: As an efficient approach to proactively monitoring network dynamics, automatically analyzing network data, and predicting network usage, machine learning has been widely deployed. This enables the networks to be efficiently and autonomously coped with in SDN/NFV environment. In particular, network intelligent technology can be adopted into the infrastructure management, network operations, and service assurance. In this paper, we study the automatic network data classification based on machine learning, where several machine learning algorithms are deployed to automatically classify real network traffic data collected from ONOS (Open Network Operating System) platform. From the experiment results with simple network topology, we conclude that machine learning algorithms can effectively classify the network traffic data. However, it is also observed machine algorithms may only show a limited performance in practice if they are blindly deployed. This is because there exists not only the data that needs to be delivered to the receivers but also the data required for network maintenance in a real network system. Therefore, it is essential to develop machine learning algorithms that explicitly consider the characteristics of real network traffic data in target network scenarios.
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Citations
A comparative study on online machine learning techniques for network traffic streams analysis
TL;DR: In this paper , the authors investigate and compare the OL techniques that facilitate data stream analytics in the networking domain and highlight the advantages of online learning in this regard, as well as the challenges associated with OL-based network traffic stream analysis, e.g., concept drift and the imbalanced classes.
65
Traffic-aware dynamic controller placement in SDN using NFV
G. Ramya,R. Manoharan +1 more
TL;DR: The proposed approach effectively combines SDN, NFV and ML for the better achievement of network automation.
13
Machine-Learning-Based Traffic Classification in Software-Defined Networks
Rehab H. Serag,Mohamed S. Abdalzaher,H. A. Elsayed,M. Sobh,Moez Krichen,Mahmoud M. Salim +5 more
TL;DR: This research explores the integration of SDN and ML for improved network performance and QoS. It primarily investigates ML classification methods for traffic classification and highlights their superiority over traditional methods. The study also examines the benefits of dynamic and adaptive TC using ML algorithms, security enhancements through anomaly and intrusion detection, and QoS integration challenges.
11
A Comprehensive Survey on Machine Learning using in Software Defined Networks (SDN)
TL;DR: In this paper , the authors collected the papers published in Springer, Elsevier, IEEE, and ACM and addressed SDN and ML between 2016 and 2023 and organized them based on the solutions, evaluation parameters, and evaluation environments.
Machine Learning with Variable Sampling Rate for Traffic Prediction in 6G MEC IoT
Rongqun Peng,Xiuhua Fu,Tian Ding +2 more
TL;DR: Wang et al. as discussed by the authors investigated the impact of variable sampling rate on traffic prediction and proposed a VSR-LSTM algorithm for real-time prediction of network traffic.
References
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TL;DR: In this article, the authors discuss the challenges and benefits of adopting big data analytics, machine learning, and artificial intelligence in the next-generation communication systems and discuss the data sources and strong drivers for the adoption of the data analytics and the role of ML, Artificial Intelligence in making the system intelligent regarding being self-aware, self-adaptive, proactive and prescriptive.
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A Self-Adaptive Deep Learning-Based System for Anomaly Detection in 5G Networks
Lorenzo Fernández Maimó,Ángel Luis Perales Gómez,Félix J. García Clemente,Manuel Gil Pérez,Gregorio Martínez Pérez +4 more
TL;DR: A novel 5G-oriented cyberdefense architecture to identify cyberthreats in 5G mobile networks efficient and quickly enough and can self-adapt the anomaly detection system based on the volume of network flows gathered from 5G subscribers’ user equipments in real-time and optimizing the resource consumption.
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TL;DR: A software defined network (SDN) based intelligent model that can efficiently manage the heterogeneous infrastructure and resources and develop a variety of schemes to improve traffic control, subscriber management, and resource allocation is proposed.
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Machine Learning in Software Defined Networks: Data collection and traffic classification
Pedro Amaral,Joao Dinis,Paulo Pinto,Luis Bernardo,João Manuel R. S. Tavares,Henrique São Mamede +5 more
- 01 Nov 2016
TL;DR: This work describes a simple architecture deployed in an enterprise network that gathers traffic data using the OpenFlow protocol and presents the data-sets that can be obtained and shows how several ML techniques can be applied to it for traffic classification.
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