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

TL;DR

This paper surveys recent advances in unsupervised machine learning techniques applied to networking, highlighting their potential to improve network performance without relying on labeled data, and discusses future research challenges.

Contribution

It provides a comprehensive overview of unsupervised learning applications in networking, synthesizing recent research and identifying open issues and future directions.

Findings

Unsupervised learning enhances traffic classification and anomaly detection.

Recent techniques enable automated, label-free network management.

Open challenges include scalability and interpretability of models.

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.