A Machine Learning Based Intrusion Detection System for Software Defined 5G Network
This addresses security challenges for software-defined 5G networks, but it is incremental as it builds on existing methods.
The paper tackles the problem of intrusion detection in software-defined 5G networks by proposing an intelligent system that combines centralized control with machine learning, achieving better performance as proven by evaluation results.
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.