A Real-Time Intrusion Detection System for Software Defined 5G Networks

The philosophy that founds the world of the Internet of Things apparently becomes essential for the projected permanently connected world. The 5G data networks are supposed to dramatically improve the actual 4G networks’ real world significance, which makes them fundamental for the next generation networks of IoT devices. The academic and industrial effort to improve the 5G technological standards considers various routes. Thus, this paper presents the state-of-the-art concerning the development of the standards that model the 5G networks. It values the authors’ experience that was gathered during the implementation of the Vodafone Romania 5G networked services. It puts this acquired experience in context by reviewing the relevant similar work, the relevant technologies, and it describes the research directions and difficulties that will probably influence the design and implementation of large 5G data networks. Consequently, the paper presents a machine learning-based real time intrusion detection system, which has been effectively tested in the context of a 5G data network. The intelligent intrusion detection system considers the creation of software defined networks, and it uses artificial intelligence based models. It is able to detect unknown intrusions through the usage of machine learning-based software components. The system has been assessed and the results prove that it achieves superior performance with a lower overhead in comparison to similar approaches, which allows it to be deployed on real-time 5G networks.