Shallow vs. Deep Learning: Prioritizing Efficiency in Next Generation Networks

With the exponential growth of mobile network traffic and diverse application demands, traditional network management methods struggle to meet the stringent requirements of 5th Generation (5G) and Beyond-5G (B5G) networks. Artificial Intelligence (AI), particularly Deep Learning (DL), has emerged as a promising solution. However, large and complex DL models can be computationally expensive for real-time applications. This paper investigates the potential of shallow Machine Learning (ML) models for 5G/B5G tasks. We compare the performance and training/inference time of shallow ML models with state-of-the-art DL models in two key tasks: network slicing attribution and Intrusion Detection Systems (IDS). The results demonstrate that shallow models achieve comparable performance with significantly faster training and prediction, leading to an acceleration of over 90% in most cases.