@article{MDE871AA6,
title = "CNN-Based Physical Layer Authentication against Impersonation Attacks",
journal = "The Journal of Korean Institute of Communications and Information Sciences",
year = "2025",
issn = "1226-4717",
doi = "10.7840/kics.2025.50.8.1172",
author = "Hanol Oh, Jihyeon Yoon, Jihwan Moon, Taehoon Kim, Inkyu Bang",
keywords = "Physical Layer Authentication (PLA), Convolutional Neural Network (CNN), Impersonation Attack, Channel State Information (CSI), IQ Imbalance",
abstract = "In this paper, we propose a convolutional neural network (CNN)-based physical layer authentication (PLA) scheme as a countermeasure against impersonation attacks in WLAN (Wireless Local Area Network). We evaluate the proposed PLA scheme in terms of attack detection rate through experiments. The main contributions are as follows: (1) We implement the Evil Twin attack which is one of the impersonation attacks in WLAN; (2) We implement the channel state information (CSI)-based PLA scheme and perform experiments to measure attack detection rates in both static and dynamic channel environments; (3) We finally propose the CNN-based PLA scheme exploiting the unique IQ imbalance of hardware and evaluate its performance through extensive simulations."
}