Communications in Computer and Information Science
Performance prediction in wireless mobile networks is essential for diverse purposes in network management and operation. Particularly, the position of mobile devices is crucial to estimating the performance in the mobile communication setting. With its importance, this paper investigates mobile communication performance based on the coordinate information of mobile devices. We analyze a recent 5G data collection and examine the feasibility of location-based performance prediction. As location information is key to performance prediction, the basic assumption of making a relevant prediction is the correctness of the coordinate information of devices given. With its criticality, this paper also investigates the impact of position falsification on the ML-based performance predictor, which reveals the significant degradation of the prediction performance under such attacks, suggesting the need for effective defense mechanisms against location spoofing threats.
Ministry of Science, ICT and Future Planning
Location spoofing, Machine learning, Mobile networks, Performance prediction, Position falsification
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Nikhil Sai Kanuri, Sang Yoon Chang, Younghee Park, Jonghyun Kim, and Jinoh Kim. "Impact of Location Spoofing Attacks on Performance Prediction in Mobile Networks" Communications in Computer and Information Science (2022): 107-119. https://doi.org/10.1007/978-3-031-24049-2_7