A Deep Learning-Based Intelligent, Intrusion Detection System for the Internet of, Medical Things

The Internet of Medical Things (IoMT) has significantly improved healthcare services by enabling the seamless connectivity of medical devices for data transmission and monitoring. However, the increase in connected medical devices also brings vulnerabilities, making security a critical concern. This project presents an Intelligent Intrusion Detection System (IDS) for IoMT based on deep learning techniques, aiming to detect and mitigate cyber threats in real-time. The system leverages four machine learning algorithms: Random Forest, LightGBM, XGBoost and two hybrid models combining Convolutional Neural Networks (CNN) as feature extractors with Random Forest and LightGBM, XGBoost classifiers. These models are applied to detect anomalies and identify potential threats within IoMT networks by analyzing various device-generated data patterns. The project utilizes CNN-based feature extraction to capture complex patterns in the data, enhancing the performance of traditional machine learning models. The models are trained and evaluated using Python, with libraries such as scikit-learn for Random Forest and LightGBM, XGBoost and Keras for CNN implementation. The IDS aims to provide a robust, scalable solution to ensure the security of IoMT systems, enabling real-time threat detection, minimizing risks, and protecting patient data. By combining both classical and deep learning techniques, the project explores the potential of advanced security measures for IoMT and emphasizes the growing importance of intelligent intrusion detection in modern healthcare environments.

Keywords: Internet of Medical Things (IoMT), Intrusion Detection System, Random Forest, LightGBM, XGBoost CNN, Feature Extraction, Deep Learning, Machine Learning, Cybersecurity, Healthcare Security, Anomaly Detection, Real-Time Threat Detection, Python.