TY  - EJOU
AU  - Pawana, I Wayan Adi Juliawan 
AU  - Abella, Vincent 
AU  - Miole, Eldridge Aaron 
AU  - Park, Hoonyong 
AU  - You, Ilsun 

TI  - AI-Native Security for 5G and 6G O-RAN: A Survey of Learning-Based Defense against False Base Stations, DDoS, and Signaling Storms
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2026
VL  - 148
IS  - 3
SN  - 1526-1506

AB  - Fifth- and sixth-generation cellular networks remain exposed to a spectrum of threats ranging from the stealthy and targeted false base station (FBS) to the systemic distributed denial-of-service (DDoS) and signaling-storm attacks. The elevation of artificial intelligence (AI) to a native network capability by the International Telecommunication Union Radiocommunication Sector (ITU-R) framework for International Mobile Telecommunications-2030 (IMT-2030) has made learning-based defense the dominant response. This survey examines that response across the three threats jointly, advancing as a design position the view that effective defense is best served when AI is integrated into the radio access network (RAN) and core as a native function rather than appended as an external classifier. We organize the literature by five technique families, namely classical machine learning, deep learning, generative models, federated learning, and large language and agentic models, and for each we record not only how it detects an attack but where in the Open RAN (O-RAN) architecture it is deployed, mapping the surveyed methods onto the user equipment, the near-real-time and non-real-time RAN Intelligent Controller, and the 5G-core network function. Three observations organize the account: the three threats form a data-scarcity gradient that illuminates the progression of techniques; the deployment dimension, the question of where and how, is the one the primary literature most often omits and the one a practitioner most needs; and several threat-technique combinations that the architecture permits remain unexplored. Drawing on a semi-systematic review of 177 studies, the survey consolidates the datasets and reported performance that make learning feasible, examines the adversarial robustness of the defenses themselves, and sets out a forward agenda toward quantum-AI-native defense within the RAN.
KW  - 5G; 6G; open RAN; false base station; DDoS; signaling storm; AI-native security; federated learning; large language models

DO  - 10.32604/cmes.2026.087445