A Hybrid Genetic Algorithm-Based Random Forest Model for Intrusion Detection Approach in Internet of Medical Things

Abstract

The Internet of Medical Things (IoMT) is a bio-network of associated medical devices, which is slowly improving the healthcare industry by focusing its abilities on enhancing personal healthcare benefits with medical data. Moreover, the IoMT tries to deliver sufficient and more suitable medical services at a low cost. With the rapid growth of technology, medical instruments that are widely used anywhere are likely to increase security issues and create safe data transmission issues through resource limitations and available connectivity. Moreover, the patients probably face the risk of different forms of physical harm because of IoMT device attacks. In this paper, we present a secure environment for IoMT devices against cyber-attacks for patient medical data using a new IoMT framework with a hybrid genetic algorithm-based random forest (GA-RF) model. The proposed algorithm achieved better results in terms of accuracy (99.999%), precision, and recall (100%, respectively) to detect cyber-attacks based on two NSL-KDD and UNSW_2018_IoT_Botnet data sets than the other machine learning algorithms.

Keywords

• Internet of Medical Things (IoMT)
• intrusion detection system
• machine learning
• random forest
• genetic algorithm