Machine Learning Approaches to Wireless Attack Detection
John Chirillo · 2025
This chapter explores machine language (ML) and its role in securing wireless communications within IoMT environments. It covers some key advancements in ML, its integration with existing security infrastructure, ethical considerations, and case studies that help highlight its impact on healthcare cybersecurity. The chapter also explores how ML enhances wireless attack detection, focusing on anomaly detection, feature extraction, real-time analysis, and attack prediction features. It aims to help us make more informed decisions about choosing technologies and solutions incorporating machine learning features. The chapter discusses key features in wireless attack detection, how to collect and preprocess data, and the metrics used to evaluate model performance. It ends with some case studies of ML in healthcare applications that the author researched in the industry. These case studies demonstrate the potential of ML to address diverse wireless security challenges in healthcare.