Impact of Location Spoofing Attacks on Performance Prediction in Mobile Networks Chapter

abstract

• AbstractPerformance 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.

authors

• Kim, Jinoh

author list (cited authors)

• Kanuri, N. S., Chang, S., Park, Y., Kim, J., & Kim, J.

citation count

• 0

complete list of authors

• Kanuri, Nikhil Sai||Chang, Sang-Yoon||Park, Younghee||Kim, Jonghyun||Kim, Jinoh

Book Title

• Silicon Valley Cybersecurity Conference

publication date

• 2022

publisher

• Springer Nature  Publisher

published in

• Communications in Computer and Information Science  Journal

keywords

• Location Spoofing
• Machine Learning
• Mobile Networks
• Performance Prediction
• Position Falsification

Digital Object Identifier (DOI)

• 10.1007/978-3-031-24049-2_7

International Standard Book Number (ISBN) 13

• 978-3-031-24048-5

start page

• 107

end page

• 119

volume

• 1683

URL

• http://dx.doi.org/10.1007/978-3-031-24049-2_7