Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics
Sugumar Rajendran
Saveetha University
Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Machine learning for adaptive intrusion detection system towards secure internet of things enabled healthcare systems Omar Isam Al Mrayat; Malik Jawarneh; D. Vetrithangam; Pallavi Sunil Bangare; Khaled Abdulwahab Zaid Alyamani; Reema Goyal; Sugumar Rajendran; Shubham Kumar; Yuvaraj Kunnathur Periyasamy; Abhishek Raghuvanshi
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.10512

Abstract

Internet of medical things (IoMT) ties medical sensors, actuators, wearable, and communication technologies to provide an improved healthcare delivery process by means of constant patient monitoring and decision-making. The trend has resulted in an increase in its use because of the increasing prevalence of chronic diseases and aging, and the need to have cost-effective healthcare services. Nonetheless, the networks provided by the IoMT are prone to cyberattacks under the considerable threat as both process huge volumes of sensitive health data, and do not have the resources to do it. In this paper, the author suggests a new adaptive intrusion detection system (IDS) as a way to enhance the security of IoMT-enabled healthcare environments. The proposed model the pre-treats NSL-KDD dataset, filling gaps and transmitting nominal values. Particle swarm optimization (PSO) is used in the optimal selection of features and artificial neural network (ANN), K-nearest neighbors (KNN), and random forest (RF) classifiers are used in intrusion detection. Experimental performance proves that the PSO-ANN model can perform better with a high accuracy of 99.54%, sensitivity of 99.31% and specificity of 97.32% on average with high protection of intelligent healthcare systems.
Co-Authors Abhishek Raghuvanshi D. Vetrithangam Khaled Abdulwahab Zaid Alyamani Malik Jawarneh Omar Isam Al Mrayat Pallavi Sunil Bangare Reema Goyal Shubham Kumar Yuvaraj Kunnathur Periyasamy