WiIID: Wi-Fi Based Intelligent Indoor Intrusion Detection With Tensor Decomposition

Wi-Fi sensing has emerged as a promising paradigm for indoor intrusion detection, as it offers a robust and high-accuracy solution without the need for extra hardware deployment. However, existing schemes often compromise the inherent structure of channel state information (CSI) during feature extraction through lossy preprocessing, causing high false alarm rates and poor generalization. As a remedy, we propose a novel tensor-based framework for indoor intrusion detection, which enables reliable perception of fine-grained human activities through structured feature extraction, even in motion-ambiguous scenarios. Our approach integrates tensor-based feature extraction, multi-dimensional feature consolidation, and a modified deep learning (DL) network for accurate intrusion recognition. To validate our framework, we collected a comprehensive through-wall CSI dataset under the IEEE 802.11n standard, encompassing five common human activities in realistic scenarios. Extensive experimental results demonstrate the superior performance of our method compared to existing state-of-the-art schemes.