Attacks and failures prediction framework for a collaborative 5G mobile network

Abstract

Although mobile technologies keep evolving through years, Fault management and cyber-security management in mobile networks are still treated as separated notions with different blocks and different approaches whereas in practice, they are highly correlated. In this paper, we propose a framework that takes into account the correlation between these two management systems. The framework is based on several prediction agents where each agent is composed of a security predictor, a fault predictor and a generic anomaly detection model. A re-enforcement process allows to enhance the reliability of the machine learning training and prediction phases of the different predictors. Besides, each agent can collaborate with its neighborhood for a more resilient network. An application of this framework to 5G architecture is proposed by mapping the components of our framework with network slices. Finally, an experimentation is held over a testbed that we set up on openstack in order to forecast future anomalies related to proxy overload, latency violation in call session network functions and to excessive usage of memory. The training is achieved with ARIMA and deep learning models with promising results.