Segurança em Redes 5G: Oportunidades e Desafios em Detecção de Anomalias e Predição de Tráfego Baseadas em Aprendizado de Máquina

Abstract

This chapter focuses on approaching and contextualizing the security of the fifth-generation (5G) mobile networks, discussing network anomaly detection techniques through hybrid tools. Classical techniques for prediction, such as time series regression analysis and the Hidden Markov Model, are revisited. New anomaly detection and traffic prediction techniques based on deep learning are presented, such as recurrent neu-ral networks, neural networks with long short-term memory, and convolutional neural networks. Finally, the challenges and new paradigms of the next-generation networks (6G) are presented. We also present a case study with a practical exercise to develop an example of anomaly detection and traffic prediction through open source and free tools.