Real time Anomaly Detection In IoMT Networks Using Stacking Model For A HealthCare Application

Sultan Saleem A; Janarthanan S; Hamalesh SV; Krishnakumar S; Kipson I

doi:10.36948/ijfmr.2026.v08i02.73740

Real time Anomaly Detection In IoMT Networks Using Stacking Model For A HealthCare Application

Author(s)	Prof. Sultan Saleem A, Mr. Janarthanan S, Mr. Hamalesh SV, Mr. Krishnakumar S, Mr. Kipson I
Country	India
Abstract	The Internet of Medical Things (IoMT) enables real-time patient monitoring and improves healthcare services, but it also introduces significant cybersecurity risks. This paper proposes a real-time anomaly detection model using machine learning techniques to identify and mitigate cyber threats in IoMT networks. A new healthcare-specific dataset is developed by combining medical data with network attack patterns, including falsification and Denial of Service (DoS) attacks. Multiple machine learning algorithms are evaluated, and a stacking ensemble model integrating XG Boost, Random Forest, and Artificial Neural Networks is proposed to enhance detection performance. Experimental results show that the proposed model achieves high accuracy, precision, recall, and F1-score, outperforming individual models. Real-time analysis further demonstrates the model’s effectiveness in detecting anomalies during live data transmission, making it suitable for securing IoMT environments.
Keywords	The Internet of Medical Things (IoMT) enables real-time patient monitoring and improves healthcare services, but it also introduces significant cybersecurity risks. This paper proposes a real-time anomaly detection model using machine learning techniques to identify and mitigate cyber threats in IoMT networks. A new healthcare-specific dataset is developed by combining medical data with network attack patterns, including falsification and Denial of Service (DoS) attacks. Multiple machine learning algorithms are evaluated, and a stacking ensemble model integrating XGBoost, Random Forest, and Artificial Neural Networks is proposed to enhance detection performance. Experimental results show that the proposed model achieves high accuracy, precision, recall, and F1-score, outperforming individual models. Real-time analysis further demonstrates the model’s effectiveness in detecting anomalies during live data transmission, making it suitable for securing IoMT environments.
Field	Computer > Network / Security
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-09
DOI	https://doi.org/10.36948/ijfmr.2026.v08i02.73740

View / Download PDF File

E-ISSN 2582-2160

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 8 Isu 2 Cover Page Vol 8 Isu 1 Cover Page Vol 7 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Real time Anomaly Detection In IoMT Networks Using Stacking Model For A HealthCare Application

Share this