Original article CNN-based RF fingerprinting for WLAN impersonation attack detection

Paper ID: EIJTEM_2026_13_4_1-10

Author's Name: Animikh Ahmed Proteek, Khondokar Habibul Kabir, Hassan Ahmad Mughees, Mateeb Talib Chaudhary

Volume: 13

Issue: 4

Year: 2026

Page No: 1-10

Abstract:

This is because wireless local area networks are more susceptible to impersonation and rogue access point attacks, which circumvent conventional security measures. This paper explores radio frequency fingerprinting with a convolutional neural network physical-layer security solution in detecting WLAN impersonation. Significant signal characteristics due to hardware are obtained and evaluated in realistic conditions in the channel. A CNN-based classification model is established to determine legitimate and impersonated routers. Results of the experiment show that it can be successfully classified, the separability of features is strong, and the accuracy of detection increases with signal-to-noise ratio. The results put into focus the promise of deep learning-aided RF fingerprinting as a secondary measure to improve the WLAN security.

Keywords: WLAN Impersonation Detection, RF Fingerprinting, Convolutional Neural Networks (CNN), Physical Layer Security, Rogue Access Point Detection, Deep Learning in Wireless Security, Wireless Intrusion Detection Systems, Signal-to-Noise Ratio (SNR) Analysis

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