Intrusion Detection System for Healthcare Systems Using Medical and Network Data: A Comparison Study

Abstract

Introducing IoT systems to healthcare applications has made it possible to remotely monitor patients' information and provide proper diagnostics whenever needed. However, providing high-security features that guarantee the correctness and confidentiality of patients' data is a significant challenge. Any alteration to the data could affect the patients' treatment, leading to human casualties in emergency conditions. Due to the high dimensionality and prominent dynamicity of the data involved in such systems, machine learning has the promise to provide an effective solution when it comes to intrusion detection. However, most of the available healthcare intrusion detection systems either use network flow metrics or patients' biometric data to build their datasets. This paper aims to show that combining both network and biometric metrics as features performs better than using only one of the two types of features. We have built a real-time Enhanced Healthcare Monitoring System (EHMS) testbed that monitors the patients' biometrics and collects network flow metrics. The monitored data is sent to a remote server for further diagnostic and treatment decisions. Man-in-the-middle cyber-attacks have been used, and a dataset of more than 16 thousand records of normal and attack healthcare data has been created. The system then applies different machine learning methods for training and testing the dataset against these attacks. Results prove that the performance has improved by 7% to 25% in some cases, and this shows the robustness of the proposed system in providing proper intrusion detection.

A Self-Learning Approach for Detecting Intrusions in Healthcare Systems

“…Several papers have already studied the cybersecurity and privacy issues of the healthcare ecosystem. Some of them are listed in [5]- [8]. In particular, T. Yaqoob et al in [5] provide a comprehensive study about the vulnerabilities of the smart medical devices and discuss relevant countermeasures.…”

“…Sun et al in [7] introduce a survey regarding the cybersecurity challenges, requirements and threats related to IoMT, thus identifying directions for future research works. Finally, in [8], A. Hady et al present a thorough review about the Intrusion Detection Systems (IDS) in the healthcare area. Below, we analyse further some notable cases.…”

Safety Score as an Evaluation Metric for Machine Learning Models of Security Applications

“…Even We have used a dataset collected from a healthcare testbed built at Washington University in St. Louis. The testbed includes monitoring sensors that collect the patient's biometrics and connect to a server in a lab-scale IoMT system [15], [16]. The dataset includes man-in-the-middle injection attacks and benign IoMT traffic.…”

Security of Things Intrusion Detection System for Smart Healthcare

“…On the other hand, Machine Learning (ML), which is closely associated with computational statistics and one major aspect of the Security of Things (SoTs), has been presented to several applications to cybersecurity for the analysis of hybrid networks, comprised of both anomaly detection and the detection of data mismanagement [1]. The ML technique is apparently becoming the most promising approach to deal with security glitches and several hidden (otherwise known as zero-day) attacks in healthcare systems [2]. The technique can recognise attacks merely by monitoring the modifications of data or simply by detecting alterations in the features of the network's traffic [3].…”