SMDFbs: Specification-Based Misbehavior Detection for False Base Stationsopen access
- Authors
- Park, Hoonyong; Astillo, Philip Virgil Berrer; Ko, Yongho; Park, Yeongshin; Kim, Taeguen; You, Ilsun
- Issue Date
- Dec-2023
- Publisher
- MDPI
- Keywords
- false base station; 5G; intrusion detection system; specification-based detection; machine learning
- Citation
- SENSORS, v.23, no.23
- Journal Title
- SENSORS
- Volume
- 23
- Number
- 23
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/26614
- DOI
- 10.3390/s23239504
- ISSN
- 1424-8220
1424-3210
- Abstract
- The advancement of cellular communication technology has profoundly transformed human life. People can now watch high-definition videos anytime, anywhere, and aim for the implementation of advanced autonomous driving capabilities. However, the sustainability of such an environment is threatened by false base stations. False base stations execute attacks in the Radio Access Network (RAN) of cellular systems, adversely affecting the network or its users. To address this challenge, we propose a behavior rule specification-based false base station detection system, SMDFbs. We derive behavior rules from the normal operations of base stations and convert these rules into a state machine. Based on this state machine, we detect network anomalies and mitigate threats. We conducted experiments detecting false base stations in a 5G RAN simulator, comparing our system with seven machine learning-based detection techniques. The experimental results showed that our proposed system achieved a detection accuracy of 98% and demonstrated lower overhead compared to other algorithms.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - ETC > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/26614)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.