SECURE INTRUSION DETECTION SYSTEM FOR INTERNET OF MEDICAL THINGS USING RANDOM FOREST CLASSIFIER AND ELLIPTIC CURVE CRYPTOGRAPHY

Abstract

The research that was carried out by us aims to improve IoMT (Internet of Medical Things) security by adding a Random Forest Classifier-based IDS (Intrusion Detection System) and ECC (Elliptic Curve Cryptography). IDS detects and classifies intrusions in IoMT networks, and with Random Forest Classifier, it efficiently analyses network traffic patterns with high accuracy. ECC ensures that the data that is being transferred via network is confidential and maintains its integrity. Experiments in a simulated IoMT environment showed that the Random Forest Classifier achieves a high detection rate for attacks like DoS (Denial of Service) and MITM (Man in the Middle) while maintaining a low false positive rate. ECC provides strong security measures, protecting critical data from unauthorized access. Our research contributes to developing secure IoMT systems, ensuring the integrity of IoMT device communications against evolving cyber threats.