Enabling Machine Learning with Service Function Chaining for Security Enhancement at 5G Edges

With massive sorts of terminals, devices, and machines connecting to 5G, a tremendous surge of data makes cyber-security a pressing issue, and conventional countermeasures are facing unprecedented challenges. Recently, with the rise of ML (Machine Learning) and SDN/NFV-based (Software-Defined Networks/Network Functions Virtualization) SFC (Service Function Chaining) techniques, how to leverage them for security enhancement in MEC (Multi-access/Mobile Edge Computing) has received much attention. Hence, in this article, we first propose an elastic framework to integrate ML with virtualized SFC, aiming at smart and efficient provision of different services at MEC. Then, we propose an ML-based anomaly detection algorithm used as a kind of service policy for SFC classifiers, which guides the latter for quick traffic classification and subsequent redirections of attack flows. Finally, we build a corresponding prototype system and evaluate the performance of the proposed algorithm through extensive experiments. Related results have confirmed the feasibility and advantages of the proposed framework and algorithm.

Automated summary

The article discusses leveraging machine learning and virtualized SFC techniques to enhance security in MEC, proposing an elastic framework and ML-based anomaly detection algorithm, which are validated through experiments to confirm feasibility and advantages.