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

doi:10.1109/aset60340.2024.10708692

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

DOI: 10.1109/aset60340.2024.10708692 URL

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

Feature selectionComputer scienceInformation gainThe InternetArtificial intelligenceSelection (genetic algorithm)Feature (linguistics)Machine learningInternet of Things

CCDS Authors

Ashraful Islam, PhD

Center Director, CCDS
Director, HCI Wing

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh (IUB)

Research Interests

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

References

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11.Aleksandr Ometov, Vitaly Petrov, Sergey Bezzateev, Sergey Andreev, Yevgeni Koucheryavy, Mário Gerla. (2019). Challenges of Multi-Factor Authentication for Securing Advanced IoT Applications. IEEE Network, 33(2), 82–88[10.1109/mnet.2019.1800240]
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13.Fazlullah Khan, Mian Ahmad Jan, Ryan Alturki, Mohammad Dahman Alshehri, Syed Tauhidullah Shah, Ateeq Ur Rehman. (2023). A Secure Ensemble Learning-Based Fog-Cloud Approach for Cyberattack Detection in IoMT. IEEE Transactions on Industrial Informatics, 19(10), 10125–10132[10.1109/tii.2022.3231424]
14.Janmenjoy Nayak, Saroj K. Meher, Alireza Souri, Bighnaraj Naik, S. Vimal. (2022). Extreme learning machine and bayesian optimization-driven intelligent framework for IoMT cyber-attack detection. The Journal of Supercomputing, 78(13), 14866–14891[10.1007/s11227-022-04453-z]
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17.Yahya Rbah, Mohammed Mahfoudi, Younes Balboul, Mohammed Y. Fattah, Saïd Mazer, Moulhime Elbekkali, Benaissa Bernoussi. (2022). Machine Learning and Deep Learning Methods for Intrusion Detection Systems in IoMT: A survey. 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), 1–9[10.1109/iraset52964.2022.9738218]
18.Shiraz Ali Wagan, Jahwan Koo, Isma Farah Siddiqui, Nawab Muhammad Faseeh Qureshi, Muhammad Attique, Dong Ryeol Shin. (2022). A Fuzzy-Based Duo-Secure Multi-Modal Framework for IoMT Anomaly Detection. Journal of King Saud University - Computer and Information Sciences, 35(1), 131–144[10.1016/j.jksuci.2022.11.007]
19.Vinayakumar Ravi, Tuan D. Pham, Mamoun Alazab. (2022). Attention-Based Multidimensional Deep Learning Approach for Cross-Architecture IoMT Malware Detection and Classification in Healthcare Cyber-Physical Systems. IEEE Transactions on Computational Social Systems, 10(4), 1597–1606[10.1109/tcss.2022.3198123]
20.Zhenyang Sun, Gangyi An, Yixuan Yang, Yasong Liu. (2023). Optimized machine learning enabled intrusion detection 2 system for internet of medical things. Franklin Open, 6, 100056[10.1016/j.fraope.2023.100056]
21.Joseph Bamidele Awotunde, Moses Kazeem Abiodun, Abidemi Emmanuel Adeniyi, Sakinat Oluwabukonla Folorunso, Rasheed Gbenga Jimoh. (2022). A Deep Learning-Based Intrusion Detection Technique for a Secured IoMT System. Communications in computer and information science, 50–62[10.1007/978-3-030-95630-1_4]
22.Subir Panja, Arup Kumar Chattopadhyay, Amitava Nag, Jyoti Prakash Singh. (2022). Fuzzy-logic-based IoMT framework for COVID19 patient monitoring. Computers & Industrial Engineering, 176, 108941[10.1016/j.cie.2022.108941]
23.Sriram Tarikere, Ian Donner, Daniel Woods. (2021). Diagnosing a healthcare cybersecurity crisis: The impact of IoMT advancements and 5G. Business Horizons, 64(6), 799–807[10.1016/j.bushor.2021.07.015]
24.Haseeb Tauqeer, Muhammad Munwar Iqbal, Aatka Ali, Shakir Zaman, Muhammad Umar Chaudhry. (2022). Cyberattacks Detection in IoMT using Machine Learning Techniques. Journal of Computing & Biomedical Informatics, 4(01), 13–20[10.56979/401/2022/80]
25.Jinjie Huang, Ningning Huang, Luo Zhang, Hongmei Xu. (2012). A method for feature selection based on the correlation analysis. , 529–532[10.1109/mic.2012.6273357]

Cited By

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]
2.Xin Liu. (2026). Towards advanced AI-based solutions for securing IoMT in smart health information systems. Scientific Reports, 16(1), 2079[10.1038/s41598-025-31850-0]
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.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]
5.Jiangang Feng, Tao Pang. (2025). A data-driven cognitive cyber-physical systems for cyber threat mitigation in medical internet of things. Peer-to-Peer Networking and Applications, 18(6)[10.1007/s12083-025-01932-x]
6.Lerina Aversano, Stefano Galantucci, Michele Mastroianni, Andrea Porcelli. (2026). A Binning-Based Approach for Intrusion Detection and Attack Classification in Internet of Medical Things Networks. Lecture notes in computer science, 351–368[10.1007/978-3-032-30530-5_22]
7.Rawan Abdulaziz AlRumaih, Tarek Moulahi, Dina M. Ibrahim. (2026). Lightweight and Transparent Intrusion Detection in the Internet of Medical Things: The Role of Explainable AI. , 5[10.3390/cmsf2026013005]
8.Nafisul Imran, Shovon Mondal. (2025). Privacy-Preserving Cyberattack Detection in IoMT Networks Using Federated Learning. , 3028–3033[10.1109/iccit68739.2025.11491509]

← All publications

Abstract

References

1.Iqbal H. Sarker. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2(3), 160[10.1007/s42979-021-00592-x]

2.Somayya Madakam, R. Ramaswamy, Siddharth Tripathi. (2015). Internet of Things (IoT): A Literature Review. Journal of Computer and Communications, 03(05), 164–173[10.4236/jcc.2015.35021]

3.Tzu-Tsung Wong, Po-Yang Yeh. (2019). Reliable Accuracy Estimates from <i>k</i>-Fold Cross Validation. IEEE Transactions on Knowledge and Data Engineering, 32(8), 1586–1594[10.1109/tkde.2019.2912815]

4.Jianchang Hu, Silke Szymczak. (2023). A review on longitudinal data analysis with random forest. Briefings in Bioinformatics, 24(2)[10.1093/bib/bbad002]

5.Željko Vujović. (2021). Classification Model Evaluation Metrics. International Journal of Advanced Computer Science and Applications, 12(6)[10.14569/ijacsa.2021.0120670]

6.Anar A. Hady, Ali Ghubaish, Tara Salman, Devrim Ünal, Raj Jain. (2020). Intrusion Detection System for Healthcare Systems Using Medical and Network Data: A Comparison Study. IEEE Access, 8, 106576–106584[10.1109/access.2020.3000421]

7.Mokhtar Mohammadi, Tarik A. Rashid, Sarkhel H. Taher Karim, Adil Hussein Mohammed, Tho Quan, Moazam Bidaki, Amir Masoud Rahmani, Mehdi Hosseinzadeh. (2021). A comprehensive survey and taxonomy of the SVM-based intrusion detection systems. Journal of Network and Computer Applications, 178, 102983[10.1016/j.jnca.2021.102983]

8.Yakub Kayode Saheed, Micheal Olaolu Arowolo. (2021). Efficient Cyber Attack Detection on the Internet of Medical Things-Smart Environment Based on Deep Recurrent Neural Network and Machine Learning Algorithms. IEEE Access, 9, 161546–161554[10.1109/access.2021.3128837]

9.Nebrase Elmrabit, Feixiang Zhou, Fengyin Li, Huiyu Zhou. (2020). Evaluation of Machine Learning Algorithms for Anomaly Detection. , 1–8[10.1109/cybersecurity49315.2020.9138871]

10.Sudarshan Nandy, Mainak Adhikari, Mohammad Ayoub Khan, Varun G Menon, Sandeep Verma. (2021). An Intrusion Detection Mechanism for Secured IoMT Framework Based on Swarm-Neural Network. IEEE Journal of Biomedical and Health Informatics, 26(5), 1969–1976[10.1109/jbhi.2021.3101686]

11.Aleksandr Ometov, Vitaly Petrov, Sergey Bezzateev, Sergey Andreev, Yevgeni Koucheryavy, Mário Gerla. (2019). Challenges of Multi-Factor Authentication for Securing Advanced IoT Applications. IEEE Network, 33(2), 82–88[10.1109/mnet.2019.1800240]

12.Huajun Wang, Genghui Li, Zhenkun Wang. (2023). Fast SVM classifier for large-scale classification problems. Information Sciences, 642, 119136[10.1016/j.ins.2023.119136]

13.Fazlullah Khan, Mian Ahmad Jan, Ryan Alturki, Mohammad Dahman Alshehri, Syed Tauhidullah Shah, Ateeq Ur Rehman. (2023). A Secure Ensemble Learning-Based Fog-Cloud Approach for Cyberattack Detection in IoMT. IEEE Transactions on Industrial Informatics, 19(10), 10125–10132[10.1109/tii.2022.3231424]

14.Janmenjoy Nayak, Saroj K. Meher, Alireza Souri, Bighnaraj Naik, S. Vimal. (2022). Extreme learning machine and bayesian optimization-driven intelligent framework for IoMT cyber-attack detection. The Journal of Supercomputing, 78(13), 14866–14891[10.1007/s11227-022-04453-z]

15.Sasanka Potluri, Shamim Ahmed, Christian Diedrich. (2018). Convolutional Neural Networks for Multi-class Intrusion Detection System. Lecture notes in computer science, 225–238[10.1007/978-3-030-05918-7_20]

16.Vinayakumar Ravi, Tuan D. Pham, Mamoun Alazab. (2023). Deep Learning-Based Network Intrusion Detection System for Internet of Medical Things. IEEE Internet of Things Magazine, 6(2), 50–54[10.1109/iotm.001.2300021]

17.Yahya Rbah, Mohammed Mahfoudi, Younes Balboul, Mohammed Y. Fattah, Saïd Mazer, Moulhime Elbekkali, Benaissa Bernoussi. (2022). Machine Learning and Deep Learning Methods for Intrusion Detection Systems in IoMT: A survey. 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), 1–9[10.1109/iraset52964.2022.9738218]

18.Shiraz Ali Wagan, Jahwan Koo, Isma Farah Siddiqui, Nawab Muhammad Faseeh Qureshi, Muhammad Attique, Dong Ryeol Shin. (2022). A Fuzzy-Based Duo-Secure Multi-Modal Framework for IoMT Anomaly Detection. Journal of King Saud University - Computer and Information Sciences, 35(1), 131–144[10.1016/j.jksuci.2022.11.007]

19.Vinayakumar Ravi, Tuan D. Pham, Mamoun Alazab. (2022). Attention-Based Multidimensional Deep Learning Approach for Cross-Architecture IoMT Malware Detection and Classification in Healthcare Cyber-Physical Systems. IEEE Transactions on Computational Social Systems, 10(4), 1597–1606[10.1109/tcss.2022.3198123]

20.Zhenyang Sun, Gangyi An, Yixuan Yang, Yasong Liu. (2023). Optimized machine learning enabled intrusion detection 2 system for internet of medical things. Franklin Open, 6, 100056[10.1016/j.fraope.2023.100056]

21.Joseph Bamidele Awotunde, Moses Kazeem Abiodun, Abidemi Emmanuel Adeniyi, Sakinat Oluwabukonla Folorunso, Rasheed Gbenga Jimoh. (2022). A Deep Learning-Based Intrusion Detection Technique for a Secured IoMT System. Communications in computer and information science, 50–62[10.1007/978-3-030-95630-1_4]

22.Subir Panja, Arup Kumar Chattopadhyay, Amitava Nag, Jyoti Prakash Singh. (2022). Fuzzy-logic-based IoMT framework for COVID19 patient monitoring. Computers & Industrial Engineering, 176, 108941[10.1016/j.cie.2022.108941]

23.Sriram Tarikere, Ian Donner, Daniel Woods. (2021). Diagnosing a healthcare cybersecurity crisis: The impact of IoMT advancements and 5G. Business Horizons, 64(6), 799–807[10.1016/j.bushor.2021.07.015]

24.Haseeb Tauqeer, Muhammad Munwar Iqbal, Aatka Ali, Shakir Zaman, Muhammad Umar Chaudhry. (2022). Cyberattacks Detection in IoMT using Machine Learning Techniques. Journal of Computing & Biomedical Informatics, 4(01), 13–20[10.56979/401/2022/80]

25.Jinjie Huang, Ningning Huang, Luo Zhang, Hongmei Xu. (2012). A method for feature selection based on the correlation analysis. , 529–532[10.1109/mic.2012.6273357]

Cited By

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]

2.Xin Liu. (2026). Towards advanced AI-based solutions for securing IoMT in smart health information systems. Scientific Reports, 16(1), 2079[10.1038/s41598-025-31850-0]

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.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]

5.Jiangang Feng, Tao Pang. (2025). A data-driven cognitive cyber-physical systems for cyber threat mitigation in medical internet of things. Peer-to-Peer Networking and Applications, 18(6)[10.1007/s12083-025-01932-x]

6.Lerina Aversano, Stefano Galantucci, Michele Mastroianni, Andrea Porcelli. (2026). A Binning-Based Approach for Intrusion Detection and Attack Classification in Internet of Medical Things Networks. Lecture notes in computer science, 351–368[10.1007/978-3-032-30530-5_22]

7.Rawan Abdulaziz AlRumaih, Tarek Moulahi, Dina M. Ibrahim. (2026). Lightweight and Transparent Intrusion Detection in the Internet of Medical Things: The Role of Explainable AI. , 5[10.3390/cmsf2026013005]

8.Nafisul Imran, Shovon Mondal. (2025). Privacy-Preserving Cyberattack Detection in IoMT Networks Using Federated Learning. , 3028–3033[10.1109/iccit68739.2025.11491509]