HIDS-IoMT: A Deep Learning-Based Intelligent Intrusion Detection System for the Internet of Medical Things

The project titled " HIDS-IoMT A Deep Learning-Based Intelligent Intrusion Detection System for the Internet of Medical Things" presents a secure and intelligent solution aimed at safeguarding the Internet of Medical Things (IoMT) against evolving cyber threats. This system leverages high-performing machine learning models—such as Random Forest, Extra Trees, Decision Tree, MLP, and LSTM classifiers—to analyze critical network flow features including flow duration, packet lengths, idle time, and transmission rates. These models are trained to distinguish between normal and anomalous traffic patterns, enabling accurate detection of potential attacks in real time. The application is built using a Flask web framework and integrated with a MySQL database, allowing users to register, upload network traffic datasets, select prediction models, and view results with ease. Upon identifying abnormal patterns, the system provides actionable security insights and compliance suggestions, such as initiating an investigation or maintaining continuous monitoring. Designed with a user-friendly interface, the platform empowers even non-technical users to interact with advanced AI-driven tools. By automating threat detection and delivering contextual recommendations, this system enhances the resilience of medical networks and contributes to the broader objective of critical infrastructure protection.

Keywords :IoMT Security, Intrusion Detection, Machine Learning, Network Traffic Analysis, Real-Time Prediction, Flask Web Application, Cybersecurity, Anomaly Detection, Medical Device Protection, Secure Data Visualization, MySQL Integration.