Abstract—This dissertation investigates the privacy implications of WiFi communication in Internet-of-Things (IoT) environments, focusing on the threat posed by out-of-network observers. Recent research has shown that in-network observers can glean information about IoT devices, user identities, and activities. However, the potential for information inference by out-of-network observers, who do not have WiFi network access, has not been thoroughly examined. The first study provides a detailed summary dataset, utilizing Random Forest for data summary classification. This study highlights the significant privacy threat to WiFi networks and IoT applications from out-of-network observers. Building on this investigation, the second study extends the research by utilizing a new set of time series monitored WiFi data frames and advanced machine learning algorithms, specifically xGboost, for Time Series classification. This extension achieved high accuracy of up to 94\% in identifying IoT devices and their working status, demonstrating faster IoT device profiling while maintaining classification accuracy. Furthermore, the study underscores the ease with which outside intruders can harm IoT devices without joining a WiFi network, launching attacks quickly and leaving no detectable footprints. Additionally, the dissertation presents a comprehensive survey of recent advancements in machine-learning-driven encrypted traffic analysis and classification. Given the challenges posed by encryption for traditional packet and traffic inspection, understanding and classifying encrypted traffic are crucial. The survey provides insights into utilizing machine learning for encrypted network traffic analysis and classification, reviewing state-of-the-art techniques and methodologies. This survey serves as a valuable resource for network administrators, cybersecurity professionals, and policy enforcement entities, offering insights into current practices and future directions in encrypted traffic analysis and classification.