Traditional Methods and Machine Learning for Anomaly Detection in Self-Organizing Networks

Abstract

The motivation behind exploring anomaly detection in self-organizing networks lies in the evolving landscape of telecommunications and network management. Conventional methods for identifying network anomalies often struggle to adapt to the dynamic and complex nature of modern self-organizing networks. The problem addressed in this research is the efficacy of anomaly detection methods in self-organizing networks (SONs) within the context of telecommunications and network management. As SONs become increasingly prevalent to meet the demands of modern, highly dynamic wireless communication systems, the need for robust anomaly detection mechanisms is paramount. Conventional anomaly detection approaches in SONs are often based on predefined rules and thresholds, which may struggle to adapt to the intricate and rapidly evolving network behaviors. These methods can result in false alarms, missed anomalies, and inefficient resource allocation. Furthermore, emerging SONs incorporate a multitude of diverse technologies, including 5G, IoT, and edge computing, compounding the complexity of anomaly detection. Contemporary machine learning techniques hold promise in addressing these challenges by enabling the automatic and adaptive detection of anomalies, leveraging the abundance of data generated in SONs. However, the suitability, performance, and scalability of these methods in dynamic and large-scale SON environments remain critical concerns. This research aims to compare and evaluate conventional anomaly detection methods against contemporary machine learning approaches in SONs to assess their accuracy, efficiency, and adaptability. The goal is to provide insights into the most effective anomaly detection strategies, ultimately enhancing network stability, minimizing downtime, and ensuring the secure and efficient operation of modern telecommunications systems.