Automated detection of cybersecurity attacks in healthcare systems with recursive feature elimination and multilayer perceptron optimization - Kilincer, Ilhan Firat; Ertam, Fatih; Sengur, Abdulkadir; Tan, Ru-San; Acharya, U. Rajendra (Elsevier, 2023)
Widespread proliferation of interconnected healthcare equipment, accompanying soft-ware, operating systems, and networks in the Internet of Medical Things (IoMT) raises the risk of security compromise as the bulk of IoMT devices are not built to withstand inter -net attacks. In this work, we have developed a cyber-attack and anomaly detection model based on recursive feature elimination (RFE) and multilayer perceptron (MLP). The RFE approach selected optimal features using logistic regression (LR) and extreme gradient boosting regression (XGBRegressor) kernel functions. MLP parameters were adjusted by using a hyperparameter optimization and 10-fold cross-validation approach was per -formed for performance evaluations. The developed model was performed on various IoMT cybersecurity datasets, and attained the best accuracy rates of 99.99%, 99.94%, 98.12%, and 96.2%, using Edith Cowan University-Internet of Health Things (ECU-IoHT), Intensive Care Unit (ICU Dataset), Telemetry data, Operating systems' data, and Network data from the testbed IoT/IIoT network (TON-IoT), and Washington University in St. Louis enhanced healthcare monitoring system (WUSTL-EHMS) datasets, respectively. The proposed method has the ability to counter cyber attacks in healthcare applications. (c) 2022 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
Keywords: Internet of Medical Things (IoMT); Recursive Feature Elimination (RFE); Multilayer Perceptron (MLP); Logistic Regression (LR); Extreme Gradient Boosting; Regressor (XGBRegressor)