RoFi: Robust WiFi Intrusion Detection via Distribution Matching
Xu Wang, Dongheng Zhang, Fengquan Zhan, Xuecheng Xie, Pengcheng Huang, Yang Hu, Yan Chen
Abstract
Intrusion detection acts as a key to in-home security, where WiFi-based systems have gained wide attention due to the ubiquitous nature of WiFi signals. While existing methods achieve impressive performance in specific environments, they are susceptible to environmental changes, especially for complex scenarios where outdoor human activities can be mistaken as intrusions. In this paper, we propose RoFi, a robust WiFi intrusion detection system which can handle more complex scenarios. It achieves this by exploring the distribution of autocorrelation function (ACF) of Channel State Information (CSI) when intrusion occurs, where likelihood ratio testing is employed to discriminate intrusion and non-intrusion scenarios, eliminating the variance of different environments. Without complex calibration, RoFi achieves an accuracy of over 97.5% in practical deployment, outperforming existing methods.
BibTeX
@inproceedings{icassp2024_rofirobustwifiin,
title = {RoFi: Robust WiFi Intrusion Detection via Distribution Matching},
author = {Xu Wang and Dongheng Zhang and Fengquan Zhan and Xuecheng Xie and Pengcheng Huang and Yang Hu and Yan Chen},
booktitle = {ICASSP 2024},
year = {2024}
}