Indoor intrusion detection is essential for various applications, including security systems and smart homes. Recently, WiFi-based detection has gained popularity due to its low cost and non-invasive nature. Current Channel State Information (CSI) based frameworks primarily use deep learning to extract gait signatures; however, their performance depends heavily on extensive labeled datasets. These methods struggle to differentiate between unlabeled and labeled data that exhibit similar features. To address this challenge, we propose a novel Two-level Feature Fusion model for Indoor Intrusion Detection (TFF-IID) utilizing commercial WiFi CSI. The model adopts a two-level structure to learn rich feature representations and introduces a Transformer with multi-head self-attention alongside a multi-scale convolution module to process sensor data. Additionally, it incorporates a self-supervised learning module to capture general normality patterns. Based on this architecture, TFF-IID achieves accurate intrusion detection using only CSI. Empirical evaluations on a private gait dataset demonstrate that TFF-IID achieves an intrusion detection accuracy of 73.5% and an F1-score of 76.2% across 10 unauthorized subjects. Moreover, cross-scenario assessments verify that the proposed model maintains high efficiency and robustness in environments characterized by diverse spatial layouts and multipath complexities. Furthermore, TFF-IID outperforms the best baseline by 19.7% and 25.7% in accuracy and F1-score, respectively.
Cui et al. (Thu,) studied this question.