Automated detection of cybersecurity attacks in healthcare systems with recursive feature elimination and multilayer perceptron optimization

• Autorzy: Firat Kilincer, Ilhan , Ertam, Fatih , Sengur, Abdulkadir , Tan, Ru-San , Rajendra Acharya, U.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

PL

Internet rzeczy medycznych; IoMT; RFE; perceptron wielowarstwowy; MLP; regresja logistyczna; LR

EN

internet of medical things; IoMT; recursive feature elimination; RFE; multilayer perceptron; MLP; logistic regression; LR; extreme gradient boosting; XGBRegressor

• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2023
• Tom: Vol. 43, no. 1
• Strony: 30--41
• Opis fizyczny: Bibliogr. 40 poz., rys., tab.

Twórcy

autor

Firat Kilincer, Ilhan

• Department of Electrical-Electronics Engineering, Technology Faculty, Firat University, Elazig, Turkey,

autor

Ertam, Fatih

• Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey,

autor

Sengur, Abdulkadir

• Department of Electrical-Electronics Engineering, Technology Faculty, Firat University, Elazig, Turkey,

autor

Tan, Ru-San

• M.B, B.S, ti, Singapore and Duke-NUS Medical School, Singapore,

autor

Rajendra Acharya, U.

• Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, Singapore,
• Biomedical Engineering, School of Science and Technology, SUSS University, Singapore
• Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)

Identyfikatory

DOI

10.1016/j.bbe.2022.11.005