WiFi CSI of indoor intrusion

This dataset comprises Channel State Information (CSI) data collected from WiFi signals in six indoor environments, specifically designed for research in indoor intrusion detection. The dataset captures fine-grained variations in wireless signals caused by human, which are indicative of potential intrusions. CSI data, extracted from commercial WiFi chipsets, provides detailed amplitude and phase information across subcarriers, enabling robust detection of subtle environmental changes. The dataset includes labeled samples for various intrusions, such as walking, running, and sneaking, as well as intrusion scenarios like unauthorized entry or loitering. It is collected in diverse indoor settings with varying layouts, furniture, and interference levels to ensure generalizability. This dataset is intended to support the development and evaluation of machine learning and signal processing algorithms for accurate, real-time indoor intrusion detection systems. It is particularly valuable for researchers working on WiFi-based sensing, human activity recognition, and security applications.