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

By Muhammad Usama, Junaid Qadir, Aunn Raza, Hunain Arif, Kok-Lim Alvin Yau, Yehia Elkhatib, Amir Hussain, Ala Al-Fuqaha. IEEE Access, 2017

433 citations recorded by the source corpus.

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 growing interest in applying unsupervised learning techniques in networking stems 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). In addition, unsupervised learning can unconstrain us from the need for 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 applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting recent advancements in unsupervised learning techniques, and describe their applications in various learning tasks, in the context of networking. We also provide a discussion on future directions and open research issues, while identifying potential pitfalls. While a few survey papers focusing on applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in the literature. Through this timely review, we aim to advance the current state of knowledge, by carefully synthesizing insights from previous survey papers, while providing contemporary coverage of the recent advances and innovations.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/4778ac8db117bdd831e3776b78de7b7d1b98de3a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Networks and Communications

Citations in the corpus

Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey (2020, background): “Ns and Private Data Anonymization in an IoT System, Data Tampering, Abnormal Behaviour Network [154] Intrusion Detection [179] Network attack detection 80.19 [180] NB Intrusion Detection 50-78 [178], [181] Anomaly Detection [182] Security of an IoT Element [183] Trac Engineering 80-90 [184] RF Intru”
6G and Beyond: The Future of Wireless Communications Systems (2020, background): “…note that there exist certain use cases such as those relating to optimizing the end-users’ Quality of Experience (QoE) [102] and network security [103] where: (i) labeled data for training is simply not present and (ii) the real-time nature of the application makes it impractical to wait for…”
Machine Learning in Aerodynamic Shape Optimization (2022, background): “Unsupervised learning algorithms analyze the data pattern and automatically set up learning rules [171, 172].”
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, background): “[39] provided an overview of the recent advances of unsupervised learning in the context of networking, such as traf-”
A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges (2019, background): “, deep neural networks) in the network field [3], [4].”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “To facilitate the analysis of raw mobile network data, unsupervised learning becomes essential in extracting insights from unlabeled data [462], so as to optimize the mobile network functionality to improve QoE.”