We design and demonstrate a method for early detection of Denial-of-Service attacks. The proposed approach takes advantage of the OpenRAN framework to collect measurements from the air interface (for attack detection) and to dynamically control the operation of the Radio Access Network (RAN). For that purpose, we developed our near-Real Time (RT) RAN Intelligent Controller (RIC) interface. We apply and analyze a wide range of Machine Learning algorithms to data traffic analysis that satisfy the accuracy and latency requirements set by the near-RT RIC. Our results show that the proposed framework is able to correctly classify genuine vs. malicious traffic with high accuracy (i.e., 95%) in a realistic testbed environment, allowing us to detect attacks already at the Distributed Unit (DU), before malicious traffic even enters the Centralized Unit (CU).
ML-based handover prediction over a real O-RAN deployment using RAN Intelligent controller
School of Computer Science and Statistics, Trinity College Dublin（都柏林圣三一大学计算机科学与统计学院）；Federal University of Espirito Santo（圣埃斯皮里图联邦大学）；Rivada Networks（里瓦达网络公司）
LLM-hRIC: LLM-empowered Hierarchical RAN Intelligent Control for O-RAN
School of Electrical and Electronic Engineering, Yonsei University（延世大学电气与电子工程学院）；Information System Technology and Design, Singapore University of Technology and Design（新加坡科技与设计大学信息系统技术与设计）
