# An Intelligent Native Network Slicing Security Architecture Empowered by Federated Learning

arXiv: https://arxiv.org/abs/2410.05312 · HTML: https://mlsanity.com/paper/2410.05312/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 74 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 9 of 9)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences](https://mlsanity.com/paper/2206.08451/) (2022)
- [5G-NIDD: A Comprehensive Network Intrusion Detection Dataset Generated over 5G Wireless Network](https://mlsanity.com/paper/2212.01298/) (2022)
- [VeReMi: A Dataset for Comparable Evaluation of Misbehavior Detection in VANETs](https://mlsanity.com/paper/1804.06701/) (2018)
- [5G network slicing using SDN and NFV- A survey of taxonomy, architectures and future challenges](https://mlsanity.com/paper/1912.02802/) (2019)
- [Enhancing Network Slicing Architectures with Machine Learning, Security, Sustainability and Experimental Networks Integration](https://mlsanity.com/paper/2307.09151/) (2023)
- [Network Service Orchestration: A Survey](https://mlsanity.com/paper/1803.06596/) (2018)

## Built on by (most cited first, 1 of 1)

- [Towards Sustainability in 6G Network Slicing with Energy-Saving and Optimization Methods](https://mlsanity.com/paper/2505.12132/) (2025)

## Abstract (arXiv)

Network Slicing (NS) has transformed the landscape of resource sharing in networks, offering flexibility to support services and applications with highly variable requirements in areas such as the next-generation 5G/6G mobile networks (NGMN), vehicular networks, industrial Internet of Things (IoT), and verticals. Although significant research and experimentation have driven the development of network slicing, existing architectures often fall short in intrinsic architectural intelligent security capabilities. This paper proposes an architecture-intelligent security mechanism to improve the NS solutions. We idealized a security-native architecture that deploys intelligent microservices as federated agents based on machine learning, providing intra-slice and architectural operation security for the Slicing Future Internet Infrastructures (SFI2) reference architecture. It is noteworthy that federated learning approaches match the highly distributed modern microservice-based architectures, thus providing a unifying and scalable design choice for NS platforms addressing both service and security. Using ML-Agents and Security Agents, our approach identified Distributed Denial-of-Service (DDoS) and intrusion attacks within the slice using generic and non-intrusive telemetry records, achieving an average accuracy of approximately 95.60% in the network slicing architecture and 99.99% for the deployed slice -- intra-slice. This result demonstrates the potential for leveraging architectural operational security and introduces a promising new research direction for network slicing architectures.