Conference Paper2024Cited by 8

Cyberattacks Classification on Internet of Medical Things Using Information Gain Feature Selection and Machine Learning

Mohammed Tahmid Hossain, Sabrina Masum Meem, Jahanggir Hossain Setu, Nabarun Halder, Ashraful Islam, Md Zahangir Alam

IEEE 2024 International Advances in Science & Engineering Technology (ASET) Multi-Conferences

IEEE, pp. 1-10

  • Good Health and Well-being — Relevance: 68%
DOI: 10.1109/aset60340.2024.10708692URL

Abstract

Cybersecurity threats pose a significant challenge in the realm of the Internet of Medical Things (IoMT). Cyber-attacks on IoMT devices can lead to the deterioration of the patient's health as well as huge financial losses for healthcare organizations. This study aims to classify six distinct IoMT cyberattacks namely Denial-of-Service (DoS), Distributed Denial-of-Service (DDoS), Reconnaissance (Recon), Spoofing, Message Queuing Telemetry Transport (MQTT) and Benign, using the recent CICIoMT2024 dataset. The study utilizes Information Gain (IG) as the feature selection technique and employs Support Vector Machine (SVM), Multidimensional Convolutional Neural Network (MultiD-CNN), Random Forest (RF), and Naive Bayes (NB) classifiers to classify the cyberattacks. MultiD-CNN outperformed all the models achieving the highest 5-fold cross-validation mean accuracy of 98.11%, mean precision of 97.73%, mean recall of 98.21%, and mean F1-score of 97.88% on the reduced feature set. However, all the models showed about 3–5% improvement in all metrics when using a reduced feature set compared to the full feature set. The findings show the significance of feature selection in enhancing the performance of the classifiers on the IoMT dataset.

Keywords

  • Random forest
  • spoofing attacks
  • naive Bayes
  • information gain feature selection
  • Support vector machine
  • Internet of Medical Things
  • cyberattack classification
  • DDoS attacks
  • reconnaissance attacks
  • denial-of-service attacks

CCDS Authors

Nabarun Halder
Nabarun Halder

Nabarun Halder

  • Research Assistant, CCDS

Published at

UbiComp, IJCNN, HCII, IEEE Access

Research Interests

Machine Learning, Deep Learning, Natural Language Processing, Health Informatics, Human-Computer Interaction

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh (IUB)

Published at

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

Research Interests

Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact

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Cited By

  1. 1.Sekione Reward Jeremiah, Abir El Azzaoui, Stefanos Gritzalis, Jong Hyuk Park. (2025). Multi-view learning and model fusion framework for threat detection in multi-protocol IoMT networks. Information Fusion, 125, 103435[10.1016/j.inffus.2025.103435]
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  3. 3.Fazliddin Makhmudov, Gayrat Juraev, Ozod Yusupov, Parvina Nasriddinova, Dusmurod Kilichev. (2026). Drift-Aware Online Ensemble Learning for Real-Time Cybersecurity in Internet of Medical Things Networks. Machine Learning and Knowledge Extraction, 8(3), 67[10.3390/make8030067]
  4. 4.Mireya Lucia Hernandez-Jaimes, Alfonso Martinez-Cruz, Kelsey Alejandra Ramírez-Gutiérrez. (2025). Transformer-Based Network Traffic Flow Embeddings for Unsupervised Anomaly Detection in IoMT environments. , 1–6[10.1109/ropec68163.2025.11353969]
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