A Machine Learning Based Intrusion Detection System for Software Defined 5G Network

TL;DR

This paper proposes an AI-powered intrusion detection system tailored for Software Defined 5G networks, enhancing security by detecting novel threats through machine learning within a centralized, flexible architecture.

Contribution

It introduces a novel intelligent intrusion detection system that integrates AI and SDN technology to improve detection of unknown attacks in 5G networks.

Findings

Achieves better detection performance than traditional systems

Effectively identifies unknown intrusions using machine learning

Provides a flexible, centralized security framework for 5G

Abstract

As an inevitable trend of future 5G networks, Software Defined architecture has many advantages in providing central- ized control and flexible resource management. But it is also confronted with various security challenges and potential threats with emerging services and technologies. As the focus of network security, Intrusion Detection Systems (IDS) are usually deployed separately without collaboration. They are also unable to detect novel attacks with limited intelligent abilities, which are hard to meet the needs of software defined 5G. In this paper, we propose an intelligent intrusion system taking the advances of software defined technology and artificial intelligence based on Software Defined 5G architecture. It flexibly combines security function mod- ules which are adaptively invoked under centralized management and control with a globle view. It can also deal with unknown intrusions by using machine learning algorithms. Evaluation results prove that the intelligent intrusion detection system achieves a better performance.