Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based model

TL;DR

This paper presents a federated learning-based intrusion detection system for 5G IoT environments using an optimized BERT model, achieving high accuracy while maintaining data privacy and resource efficiency.

Contribution

It introduces a resource-optimized BERT-based IDS tailored for federated learning in 5G IoT, with model compression and validation across various data distributions.

Findings

Achieved 97.79% accuracy in centralized setup

Model size reduced by 28.74% with minimal accuracy loss

Validated effectiveness on both IID and non-IID data scenarios

Abstract

The fifth-generation (5G) offers advanced services, supporting applications such as intelligent transportation, connected healthcare, and smart cities within the Internet of Things (IoT). However, these advancements introduce significant security challenges, with increasingly sophisticated cyber-attacks. This paper proposes a robust intrusion detection system (IDS) using federated learning and large language models (LLMs). The core of our IDS is based on BERT, a transformer model adapted to identify malicious network flows. We modified this transformer to optimize performance on edge devices with limited resources. Experiments were conducted in both centralized and federated learning contexts. In the centralized setup, the model achieved an inference accuracy of 97.79%. In a federated learning context, the model was trained across multiple devices using both IID (Independent and Identically Distributed) and non-IID data, based on various scenarios, ensuring data privacy and compliance with regulations. We also leveraged linear quantization to compress the model for deployment on edge devices. This reduction resulted in a slight decrease of 0.02% in accuracy for a model size reduction of 28.74%. The results underscore the viability of LLMs for deployment in IoT ecosystems, highlighting their ability to operate on devices with constrained computational and storage resources.