article · Artificial Intelligence Review
Telecommunication networks handle massive volumes of data, making them increasingly dynamic, complex, and vulnerable to disruptions. Conventional rule-based anomaly detection methods can no longer manage these fast-evolving environments. Modern approaches rely on artificial intelligence, particularly deep learning, to detect abnormal events, maintain security, and safeguard continuous operations. An analysis of the field highlights the progression from early detection strategies to machine learning implementations and practical deployments. Emerging tools, including Generative Adversarial Networks and Reinforcement Learning, offer significant potential to strengthen network oversight alongside advances in 5G, 6G, edge computing, and the Internet of Things. To achieve reliable performance, the adoption of hybrid models, advanced data preprocessing, and self-adaptive systems is recommended. These methods support proactive anomaly management and performance optimisation in data-driven environments.
Modern telecommunication networks underpin critical digital communication, meaning unresolved disruptions can cause severe outages and security vulnerabilities. Moving beyond legacy detection systems to artificial intelligence ensures that operators can monitor massive data flows continuously. This keeps essential communications reliable, protects connected services, and provides the operational stability required for newer technologies such as 5G, edge computing, and smart devices.
The primary beneficiaries are telecommunication operators requiring automated security and network oversight. Applications centre on real-time anomaly detection and performance management across 5G, 6G, edge, and Internet of Things networks. Because the work is an analytical review evaluating algorithms, emerging architectures, and case studies rather than presenting a standalone deployment, the commercial readiness appears to be at an applied research stage requiring operators to build or integrate recommended hybrid models.
AI-generated from the published abstract. Always read the original work before citing.
Telecommunication networks are becoming increasingly dynamic and complex due to the massive amounts of data they process. As a result, detecting abnormal events within these networks is essential for maintaining security and ensuring seamless operation. Traditional methods of anomaly detection, which rely on rule-based systems, are no longer effective in today’s fast-evolving telecom landscape. Thus, making AI useful in addressing these shortcomings. This review critically examines the role of Artificial Intelligence (AI), particularly deep learning, in modern anomaly detection systems for telecom networks. It explores the evolution from early strategies to current AI-driven approaches, discussing the challenges, the implementation of machine learning algorithms, and practical case studies. Additionally, emerging AI technologies such as Generative Adversarial Networks (GANs) and Reinforcement Learning (RL) are highlighted for their potential to enhance anomaly detection. This review provides AI’s transformative impact on telecom anomaly detection, addressing challenges while leveraging 5G/6G, edge computing, and the Internet of Things (IoT). It recommends hybrid models, advanced data preprocessing, and self-adaptive systems to enhance robustness and reliability, enabling telecom operators to proactively manage anomalies and optimize performance in a data driven environment.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1007/s10462-025-11108-x
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
