Artificial intelligence driven Wi-Fi CSI data mining: Focusing on the intrusion detection applications

In the past several years, a series of breakthrough research advancements have been achieved by leveraging wireless signals such as Wi-Fi in various emerging applications, including healthcare, behavior recognition, positioning, and target detection. Compared to traditional human behavior sensing methods, Wi-Fi signals human behavior sensing technology has many advantages, including non-line-of-sight, sensor device-free sensing, passive sensing, ease of deployment, and no need for lights. Data mining undoubtedly plays a critical role in making Wi-Fi-based human behavior detection intelligent enough to facilitate convenient services and environments. We study Wi-Fi signals mining using the data mining process and review the developmental process of Wi-Fi data mining. This covers the methods of Wi-Fi data mining, including signal acquisition, preprocessing, feature extraction to training, and classification. We then propose WHSecurity, a whole home intrusion detection and tracking system that is based on all of the methods covered above. Finally, WHSecurity includes a deep learning-based data mining process called multiview learning for the decision-making on intrusion detection and tracking. Experimental outcomes show that the WHSecurity approach performs superior in terms of intrusion detection and tracking performance.