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Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

Research output: Contribution to journalJournal article

Published

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Unsupervised Machine Learning for Networking : Techniques, Applications and Research Challenges. / Usama, Muhammad; Qadir, Junaid; Raza, Aunn; Arif, Hunain; Yau, Kok-Lim Alvin; Elkhatib, Yehia; Hussain, Amir; Al-Fuqaha, Ala.

In: arxiv.org, 19.09.2017.

Research output: Contribution to journalJournal article

Harvard

Usama, M, Qadir, J, Raza, A, Arif, H, Yau, K-LA, Elkhatib, Y, Hussain, A & Al-Fuqaha, A 2017, 'Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges', arxiv.org.

APA

Usama, M., Qadir, J., Raza, A., Arif, H., Yau, K-L. A., Elkhatib, Y., Hussain, A., & Al-Fuqaha, A. (2017). Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges. arxiv.org.

Vancouver

Usama M, Qadir J, Raza A, Arif H, Yau K-LA, Elkhatib Y et al. Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges. arxiv.org. 2017 Sep 19.

Author

Usama, Muhammad ; Qadir, Junaid ; Raza, Aunn ; Arif, Hunain ; Yau, Kok-Lim Alvin ; Elkhatib, Yehia ; Hussain, Amir ; Al-Fuqaha, Ala. / Unsupervised Machine Learning for Networking : Techniques, Applications and Research Challenges. In: arxiv.org. 2017.

Bibtex

@article{7c5c30092f0d4c7782ecd2f03dedde02,
title = "Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges",
abstract = "While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances.",
keywords = "cs.NI, cs.LG",
author = "Muhammad Usama and Junaid Qadir and Aunn Raza and Hunain Arif and Yau, {Kok-Lim Alvin} and Yehia Elkhatib and Amir Hussain and Ala Al-Fuqaha",
year = "2017",
month = sep,
day = "19",
language = "English",
journal = "arxiv.org",

}

RIS

TY - JOUR

T1 - Unsupervised Machine Learning for Networking

T2 - Techniques, Applications and Research Challenges

AU - Usama, Muhammad

AU - Qadir, Junaid

AU - Raza, Aunn

AU - Arif, Hunain

AU - Yau, Kok-Lim Alvin

AU - Elkhatib, Yehia

AU - Hussain, Amir

AU - Al-Fuqaha, Ala

PY - 2017/9/19

Y1 - 2017/9/19

N2 - While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances.

AB - While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances.

KW - cs.NI

KW - cs.LG

M3 - Journal article

JO - arxiv.org

JF - arxiv.org

ER -