Analysis of Approaches for Identifying Threats in the Internet of Medical Things (IoMT) and Exploration of Research Frontiers

The contemporary healthcare landscape has witnessed a rapid integration of the Internet of Things (IoT), particularly within the realm of medical devices, forming the Internet of Medical Things (IoMT). While IoMT offers transformative benefits, it also introduces significant security challenges. This study presents an innovative and holistic approach to comprehensively analyze and identify threats in the IoMT environment. The proposed method involves a wellstructured process encompassing data collection, preprocessing, feature extraction, and threat identification through advanced machine learning algorithms. Raw data, often noisy and redundant, is initially collected from various IoMT devices, which primarily consist of crucial medical data such as vital signs and diagnostic information. Preprocessing is then applied to clean, normalize, and transform this raw data into a suitable format for subsequent analysis. Feature extraction is pivotal to capture meaningful information, essential for effective threat identification, and involves the identification of pertinent features related to patient health, device behavior, and system interactions. Utilizing machine learning algorithms, specifically Support Vector Machine (SVM), Random Forest, and Convolutional Neural Network (CNN), the proposed method showcases superior performance in comparison to traditional methods. Additionally, the proposed method exhibits significantly lower execution times, highlighting its efficiency and suitability for real-time threat detection, a crucial requirement in the dynamic IoMT landscape. This research stands as a significant contribution to bolstering the security and reliability of IoMT systems, ensuring a safer healthcare environment for patients and practitioners.