A Study on Anomaly Detection in Wireless Communication Networks

DOI

10.2991/978-2-38476-475-4_4How to use a DOI?

Keywords

Anomaly Detection; Wireless Communication; Machine Learning

Abstract

With the rapid expansion of wireless communication networks, ensuring security and reliability has become a critical challenge. Anomaly detection plays a pivotal role in identifying potential threats, performance degradation, and unexpected behaviors within these networks. This paper provides a comprehensive review of anomaly detection techniques in wireless communication networks, covering both traditional statistical methods and advanced machine learning-based approaches. We categorize these techniques based on their detection methodologies, data sources, and application scenarios. Additionally, we discuss key challenges such as high-dimensional data processing, real-time detection, and adversarial attacks. Finally, we highlight emerging trends and future research directions to enhance anomaly detection capabilities in next-generation wireless networks.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Rongyu Yang
PY  - 2025
DA  - 2025/11/11
TI  - A Study on Anomaly Detection in Wireless Communication Networks
BT  - Proceedings of the 2025 10th International Conference on Modern Management, Education and Social Sciences (MMET 2025)
PB  - Atlantis Press
SP  - 25
EP  - 34
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-475-4_4
DO  - 10.2991/978-2-38476-475-4_4
ID  - Yang2025
ER  -