An RFE/Ridge-ML/DL based anomaly intrusion detection approach for securing IoMT system

Abstract

Smart healthcare is one of the promising areas of the Internet of Things (IoT), particularly in the case of the Covid-19 pandemic. Real-time patient monitoring and remote diagnostics facilitate better medical services to preserve human lives using Internet of Medical Things (IoMT) technology. Regardless of the numerous benefits, IoMT devices are susceptible to sophisticated cyber-attacks at a breakneck pace, which lead to tampering with healthcare data and threaten patients' lives. In a similar context, the 2022 ransomware cyber-attack on Versailles André-Mignot Hospital compromised the healthcare system and disclosed tremendous amounts of patient information. Towards this direction, most researchers have solely developed either machine learning or Deep learning algorithms to identify network traffic anomalies. Motivated by the above challenges, an effort has been made in this paper to design a Recursive Feature Elimination (RFE) integrated with machine learning paradigms and a Ridge regression merged into deep learning models for implementing accurate anomaly intrusion detection based on the real-time dataset WUSTL-EHMS. Among the paradigms used, the proposed approach confirms that the RFE-based Decision Tree (DT) outperforms state-of-the-art techniques with a training accuracy of 99 % and a testing accuracy of 97.85 % while maintaining a reduction of FAR to 0.03. In a nutshell, it has been proven that the suggested framework can be deployed to build anomaly intrusion detection, reinforcing IoMT against widespread cyber-attacks and safeguarding the integrity of advanced healthcare systems.