Securing 5G Network Slices with Adaptive Machine Learning Models as-a-Service: A Novel Approach
Roumaissa Bekkouche, Mawloud Omar, Rami Langar · 2023
5G networks are highly dynamic and non-homogeneous networks, making resource management more complex and vulnerable to different network attacks like DDoS, Port Scanning, etc. In addition, Network Slicing plays an important role in these networks in enabling a multitude of 5G applications, services, and use cases. In this context, we propose in this paper, a new approach to secure 5G network slices by developing a models' orchestrator providing an adaptive Machine-Learning (ML) models as-a-Service. Specifically, the proposed models' orchestrator is a cloud server that acts as a decision-making entity to offer on-demand adaptive ML models to detect potential attacks by tuning and adapting ML parameters and algorithms according to the characteristics of the requester devices/nodes and the real-time conditions of each network slice. We demonstrate the effectiveness of our approach through a series of experiments, by training different ML algorithms with different network slices properties. Results show that our approach provides a more efficient and effective way of securing 5G networks compared to traditional methods in terms of respecting the requirements raised by each slice/node of these networks.