Machine learning‐based IDS for software‐defined 5G network. Issue 2 (1st March 2018)

• Record Type: Journal Article
• Authors: Li, Jiaqi; Zhao, Zhifeng; Li, Rongpeng
• Is Part Of: IET networks. Volume 7:Issue 2(2018)
• Page Start: 53
• Page End: 60
• Publication Date: 2018-03-01
• Subjects: software defined networking -- computer network security -- learning (artificial intelligence) -- 5G mobile communication; machine learning‐based IDS -- software‐defined 5G network -- fifth generation networks -- intrusion detection systems -- network security -- intelligent IDS -- artificial intelligence -- security function modules -- centralised management -- centralised control -- flow‐based classification; Computer network architectures -- Periodicals; Computer network protocols -- Periodicals; Information networks -- Periodicals; Telecommunication systems -- Periodicals; 004.605
• DOI: 10.1049/iet-net.2017.0212
• Languages: English
• ISSNs: 2047-4954

Abstract

Abstract : As an inevitable trend of future fifth generation (5G) networks, software‐defined architecture has many advantages in providing centralised control and flexible resource management. However, 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 (IDSs) 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 study, the authors propose an intelligent IDS taking the advances in software‐defined technology and artificial intelligence based on software‐defined 5G architecture. It flexibly integrates security function modules which are adaptively invoked under centralised management and control with a global view. It can also deal with unknown intrusions by using machine learning algorithms. Evaluation results prove that the intelligent IDS achieves better performance with lower overhead. It is also verified that the selected machine learning algorithms show better accuracy and reduced false alarm rate in flow‐based classification.