Machine Learning based Malware Detection for IoT Networks

The advent of the Internet of Things (IoT) has instigated significant transformations within the healthcare sector. It has paved the way for cutting-edge healthcare monitoring systems. To ensure the dependability and effectiveness of these systems, it becomes imperative to engage in performance modeling and analysis. This research centers on the assessment of IoT-enabled healthcare monitoring system performance, taking into consideration diverse factors including IoT network infrastructure, communication protocols, data processing, and storage. Furthermore, the study employs machine learning techniques to appraise healthcare data collected through IoT networks. Through simulation-based methodologies, this investigation models the behavior of the system and evaluates its performance using metrics like response time, throughput, and reliability. The discoveries from this study furnish invaluable insights into the performance of the system, pinpointing areas where enhancements can elevate the overall efficiency of healthcare monitoring systems. Consequently, this research makes substantial contributions to the enhancement of efficient IoT-enabled healthcare monitoring systems, which in turn offer dependable and cost-effective healthcare solutions.