Conference Paper2025Cited by 1

Performance Analysis of Machine Learning Models for Detecting Anomalous Traffic in Internet of Medical Things

Nabarun Halder, Tunisha Yanoor Bristy, Mohammad Arshad Hossain Ratul, Md Zahangir Alam, Mehedi Hassan, Ashraful Islam, M. Ashraful Amin

2025 6th International Conference on Artificial Intelligence, Robotics and Control (AIRC)

IEEE, pp. 482-487

  • Good Health and Well-being — Relevance: 77%
DOI: 10.1109/airc64931.2025.11077493URL

Abstract

The increasing reliance on digital technologies is transforming healthcare services, with the integration of Internet of Medical Things (IoMT) devices enabling real-time patient monitoring and optimized treatment in critical conditions. However, their weak security measures create entry points for cybercriminals, risking large-scale data breaches and exposing sensitive medical and financial information. Beyond privacy concerns, such attacks can disrupt critical healthcare services, delaying life-saving treatments. Early detection and mitigation of anomalous network traffic are essential for ensuring patient safety and system reliability. In this study, we used the “IoMTTrafficData dataset 2023” to evaluate the effectiveness of various Machine Learning (ML) algorithms in detecting cyberattacks within IoMT networks. In addition, we employed an incremental feature selection strategy and investigated the impact of feature dimensionality on model performance. Features were ranked based on Information Gain (IG) scores, and four ML models: Logistic Regression (LR), Decision Tree (DT), Naïve Bayes (NB), and Random Forest (RF) were trained and evaluated using progressively larger sets of the top-ranked features (top 5, top 10, top 15, top 20, and top 25). Our findings reveal that DT and RF consistently outperformed LR and NB, achieving 99% accuracy with just the top 10 IG-selected features. Furthermore, using the top 15 or more features, both models attained a precision, recall, and F1-score of 99%, which demonstrates efficient intrusion detection in IoMT environments with optimized feature sets.

Keywords

  • Decision tree
  • Internet of Medical Things
  • Information gain
  • naive Bayes
  • Random forest
  • feature dimensionality
  • cyberattack detection
  • Feature selection
  • Logistic regression
  • network intrusion detection

CCDS Authors

Nabarun Halder
Nabarun Halder

Nabarun Halder

  • Research Assistant, CCDS

Published at

UbiComp, IJCNN, HCII, Pattern Recognition, IEEE Access

Mehedi Hassan, PhD
Mehedi Hassan, PhD

Mehedi Hassan, PhD

  • Collaborator, HCI Wing

  • Instructor

    University of Arkansas

  • Visiting Instructor

    Georgia Southern University, USA

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, Pattern Recognition, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, Pattern Recognition, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

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Cited By

  1. 1.Rui P. O. Pinto, Bruno M. C. Silva, Pedro R. M. Inácio. (2026). X-IoMT: A Cross-Layer IoMT Dataset for Anomaly Detection With FGSM- and PGD-Based Adversarial Augmentation. IEEE Internet of Things Journal, 13(10), 20797–20810[10.1109/jiot.2026.3664373]
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