{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T12:28:34Z","timestamp":1742646514836},"reference-count":0,"publisher":"SASA Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JOWUA"],"published-print":{"date-parts":[[2024,3,29]]},"abstract":"<jats:p>The threat posed by false base stations remains pertinent across the 4G, 5G, and forthcoming 6G generations of mobile communication. In response, this paper introduces a real-time detection method for false base stations employing two approaches: machine learning and specification-based. Utilizing the UERANSIM open 5G RAN (Radio-Access Network) test platform, we assess the feasibility and practicality of applying these techniques within a 5G network environment. Emulating real-world 5G conditions, we implement a functional split in the 5G base station and deploy the False Base Station Detection Function (FDF) as a 5G NF (Network Function) within the CU (Central Unit). This setup enables real-time detection seamlessly integrated into the network. Experimental results indicate that specification-based detection outperforms machine learning, achieving a detection accuracy of 99.6% compared to 96.75% for the highest-performing machine learning model XGBoost. Furthermore, specification-based detection demonstrates superior efficiency, with CPU usage of 1.2% and memory usage of 16.12MB, compared to 1.6% CPU usage and 182.4MB memory usage for machine learning on average. Consequently, the implementation of specification-based detection using the proposed method emerges as the most effective technique in the 5G environment.<\/jats:p>","DOI":"10.58346\/jowua.2024.i1.013","type":"journal-article","created":{"date-parts":[[2024,3,30]],"date-time":"2024-03-30T08:46:10Z","timestamp":1711788370000},"page":"184-201","source":"Crossref","is-referenced-by-count":2,"title":["A Study on the Implementation of a Network Function for Real-time False Base Station Detection for the Next Generation Mobile Communication Environment"],"prefix":"10.58346","volume":"15","author":[{"given":"Daehyeon","family":"Son","sequence":"first","affiliation":[]},{"given":"Youngshin","family":"Park","sequence":"additional","affiliation":[]},{"given":"Bonam","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Ilsun","family":"You","sequence":"additional","affiliation":[]}],"member":"37075","published-online":{"date-parts":[[2024,3,29]]},"container-title":["Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications"],"original-title":[],"deposited":{"date-parts":[[2024,3,30]],"date-time":"2024-03-30T08:46:15Z","timestamp":1711788375000},"score":1,"resource":{"primary":{"URL":"https:\/\/jowua.com\/wp-content\/uploads\/2024\/03\/2024.I1.013.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,29]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,3,29]]},"published-print":{"date-parts":[[2024,3,29]]}},"URL":"https:\/\/doi.org\/10.58346\/jowua.2024.i1.013","relation":{},"ISSN":["2093-5374","2093-5382"],"issn-type":[{"value":"2093-5374","type":"print"},{"value":"2093-5382","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,29]]}}}