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Summary 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. [ABSTRACT FROM AUTHOR]