Automated detection of cybersecurity attacks in healthcare systems with recursive feature elimination and multilayer perceptron optimization
Article
Kilincer, Ilhan Firat, Ertam, Fatih, Sengur, Abdulkadir, Tan, Ru-San and Acharya, U. Rajendra. 2023. "Automated detection of cybersecurity attacks in healthcare systems with recursive feature elimination and multilayer perceptron optimization." Biocybernetics and Biomedical Engineering. 43 (1), pp. 30-41. https://doi.org/10.1016/j.bbe.2022.11.005
Article Title | Automated detection of cybersecurity attacks in healthcare systems with recursive feature elimination and multilayer perceptron optimization |
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ERA Journal ID | 211872 |
Article Category | Article |
Authors | Kilincer, Ilhan Firat, Ertam, Fatih, Sengur, Abdulkadir, Tan, Ru-San and Acharya, U. Rajendra |
Journal Title | Biocybernetics and Biomedical Engineering |
Journal Citation | 43 (1), pp. 30-41 |
Number of Pages | 12 |
Year | 2023 |
Publisher | Elsevier BV |
Place of Publication | Netherlands |
ISSN | 0208-5216 |
2391-467X | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.bbe.2022.11.005 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0208521622001012 |
Abstract | Widespread proliferation of interconnected healthcare equipment, accompanying software, 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 internet 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 performed 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. |
Keywords | Regressor (XGBRegressor); Internet of Medical Things (IoMT); Recursive Feature Elimination (RFE); Multilayer Perceptron (MLP); Extreme Gradient Boosting; Logistic Regression (LR) |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Firat University, Turkey |
Duke-NUS Medical Centre, Singapore | |
Ngee Ann Polytechnic, Singapore | |
Singapore University of Social Sciences (SUSS), Singapore | |
Asia University, Taiwan |
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https://research.usq.edu.au/item/z1v8z/automated-detection-of-cybersecurity-attacks-in-healthcare-systems-with-recursive-feature-elimination-and-multilayer-perceptron-optimization
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