Feature efficiency in IoMT security: A comprehensive framework for threat detection with DNN and ML.

Journal: Computers in biology and medicine

Published Date: Jan 1, 2025

Abstract

BACKGROUND: To address critical security challenges in the Internet of Medical Things (IoMT), this study develops a feature selection framework to improve detection accuracy and computational efficiency in IoMT cybersecurity. By optimizing feature selection, the framework aims to enhance the security and operational integrity of real-time healthcare systems.

Authors

Merve Pinar

Department of Computer Engineering, Marmara University, İstanbul, Turkey.
Abdulsamet Aktas

Computer Engineering Department, Technology Faculty, Marmara University, Maltepe, Istanbul, Turkey. Electronic address: abdulsamet.aktas@marmara.edu.tr.
Eyup Emre Ulku

Computer Engineering Department, Technology Faculty, Marmara University, Maltepe, Istanbul, Turkey. Electronic address: emre.ulku@marmara.edu.tr.

Keywords

Computer Security Humans Internet of Things Machine Learning Neural Networks, Computer Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (39746295)

Feature efficiency in IoMT security: A comprehensive framework for threat detection with DNN and ML.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals