Active Attack Detection Based on Interpretable Channel Fingerprint and Adversarial Autoencoder

Abstract

This paper investigates how to build an active attack detection framework that is driven by fundamental channel modeling and practical wireless datasets. Firstly, we propose the concept of interpretable channel fingerprints (ICFs), which correspond to the spatial-temporal parameters in real physical wireless signal propagation channels. Based on this, we design an adversarial autoencoder (AAE) with a semi-supervised learning network, which takes as inputs the power spectrum of quantized ICFs and enables small sample learning multiclassification tasks for different types of wireless channel active attacks. We have experimentally verified the performance of our AAE network using the Wireless InSite ray tracing software. Our results show that the proposed semi-supervised network outperforms the fully-supervised network especially in small sample conditions. We highlight the need for careful selection of the hyperparameters for learning rate and mini-batch size, and the system parameters for the ICF power spectrum resolution. We show that the detection accuracy of the proposed AAE model can reach more than 98% with only a small number of input samples.