Intrusion Detection in IoT-Based Healthcare Using ML and DL Approaches: A Case Study

Abstract

Internet of Things (IoT) aims to make improvement is quality of human life. The use of IoT technology in healthcare services is to make the service more personalized and efficient. Sadly, this has drawn the attention of cyber attackers, who have turned IoT into a target of illicit activity, leaving the terminal nodes vulnerable to attacks. A successful secure framework is provided by an intrusion detection system (IDS), which plays a crucial role in identifying and resisting various intrusion attempts. IoT IDS has recently benefited from advancements in artificial intelligence (AI), including machine learning (ML) and deep learning (DL) approaches. This book chapter offers a current taxonomy, a summary, and analysis of significant IoT IDS research papers published to date, including a taxonomy-based categorization of the proposed solutions. In order for a researcher to quickly become familiar with the essential components of IoT IDS, it offers a structured and thorough overview of the existing IoT IDSs. A critical analysis of the machine learning and deep learning methods used to create IoT IDS is also provided in this book chapter. We review the various approaches used in each method, along with the detection strategies, validation strategies, and deployment strategies. Following a discussion of the complexity of various detection methods, intrusion deployment strategies, and their evaluation methods.