Anomaly Detection in Self-Organizing Networks: Conventional Versus Contemporary Machine Learning

Abstract

This paper presents a comparison of conventional and modern machine (deep) learning within the framework of anomaly detection in self-organizing networks. While deep learning has gained significant traction, especially in application scenarios where large volumes of data can be collected and processed, conventional methods may yet offer strong statistical alternatives, especially when using proper learning representations. For instance, support vector machines have previously demonstrated state-of-the-art potential in many binary classification applications and can be further exploited with different representations, such as one-class learning and data augmentation. We demonstrate for the first time, on a previously published and publicly available dataset, that conventional machine learning can outperform the previous state-of-the-art using deep learning by 15% on average across four different application scenarios. Our results further indicate that with nearly two orders of magnitude improvement in computational speed and an order of magnitude reduction in trainable parameters, conventional machine learning provides a robust alternative for 5G self-organizing networks especially when the execution and detection times are critical.