Distributed Learning-Based Intrusion Detection in 5G and Beyond Networks
Cheol-Hee Park, Kyungmin Park, Jihyeon Song, Jonghyun Kim
Abstract
As mobile technology has evolved over generations, communication systems have advanced along with it. Moreover, the 6th generation (6G) of mobile networks is expected to evolve into a more decentralized and open environment. Meanwhile, with these advancements in network systems, the attack surface that can be exposed to adversaries has expanded, and potential threats have become more sophisticated. To secure network sys-tems from these potential attacks, various studies have focused on intrusion detection systems. In particular, studies on artificial intelligence-based network intrusion detection systems have been actively conducted and have shown remarkable results. However, most of these studies concentrate on centralized environments and may not be suitable for deployment in distributed systems. In this paper, we propose a distributed learning-based intrusion detection system that can efficiently train predictive models in a decentralized environment and enable learning in systems with varying computing capabilities. We leveraged a state-of-the-art split learning approach, which allows for models to be trained in distributed systems with different computing resources. In our experiments, we evaluate the models using data collected in a 5G mobile network environment and demonstrate that the proposed system can be applied for network security in the next-generation mobile environment.