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

doi:10.1109/airc64931.2025.11077493

Conference Paper2025

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)

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

IEEE, pp. 482-487

DOI: 10.1109/airc64931.2025.11077493 URL

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

Computer scienceThe InternetInternet of ThingsInternet trafficArtificial intelligenceMachine learning

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

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← All publications

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)

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

IEEE, pp. 482-487

Abstract

References

1.Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone. (2017). Classification And Regression Trees. [10.1201/9781315139470]

2.Md Ekrim Hossin, Sulaiman M.N. (2015). A Review on Evaluation Metrics for Data Classification Evaluations. International Journal of Data Mining & Knowledge Management Process, 5(2), 01–11[10.5121/ijdkp.2015.5201]

3.David Lewis. (1998). Naive (Bayes) at forty: The independence assumption in information retrieval. Lecture notes in computer science, 4–15[10.1007/bfb0026666]

4.Gulraiz Javaid Joyia, Rao Muzamal Liaqat, Farooq Aftab, Saad Rehman. (2017). Internet of Medical Things (IOMT): Applications, Benefits and Future Challenges in Healthcare Domain. Journal of Communications[10.12720/jcm.12.4.240-247]

5.Prabhat Kumar, Govind P. Gupta, Rakesh Tripathi. (2020). An ensemble learning and fog-cloud architecture-driven cyber-attack detection framework for IoMT networks. Computer Communications, 166, 110–124[10.1016/j.comcom.2020.12.003]

6.Hamed Alqahtani, Iqbal H. Sarker, Asra Kalim, Syed Md. Minhaz Hossain, Sheikh Ikhlaq, Sohrab Hossain. (2020). Cyber Intrusion Detection Using Machine Learning Classification Techniques. Communications in computer and information science, 121–131[10.1007/978-981-15-6648-6_10]

7.Muhammad Muhammad Inuwa, Resul Daş. (2024). A comparative analysis of various machine learning methods for anomaly detection in cyber attacks on IoT networks. Internet of Things, 26, 101162[10.1016/j.iot.2024.101162]

8.Qasem Abu Al‐Haija, Saleh Zein-Sabatto. (2020). An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks. Electronics, 9(12), 2152[10.3390/electronics9122152]

9.Yash Shah, Shamik Sengupta. (2020). A survey on Classification of Cyber-attacks on IoT and IIoT devices. , 0406–0413[10.1109/uemcon51285.2020.9298138]

10.Nuno Oliveira, Isabel Praça, Eva Maia, Orlando Sousa. (2021). Intelligent Cyber Attack Detection and Classification for Network-Based Intrusion Detection Systems. Applied Sciences, 11(4), 1674[10.3390/app11041674]

11.Hüseyin Ahmetoğlu, Resul Daş. (2022). A comprehensive review on detection of cyber-attacks: Data sets, methods, challenges, and future research directions. Internet of Things, 20, 100615[10.1016/j.iot.2022.100615]

12.Jadel Marzouq Alsamiri, Khalid Alsubhi. (2019). Internet of Things Cyber Attacks Detection using Machine Learning. International Journal of Advanced Computer Science and Applications, 10(12)[10.14569/ijacsa.2019.0101280]

13.Kanglin Qu, Kanglin Qu, Jiucheng Xu, Qincheng Hou, Kangjian Qu, Kangjian Qu, Yuanhao Sun. (2023). Feature selection using Information Gain and decision information in neighborhood decision system. Applied Soft Computing, 136, 110100[10.1016/j.asoc.2023.110100]

14.José Areia, Ivo Afonso Bispo, Leonel Santos, Rogério Luís de C. Costa. (2024). IoMT-TrafficData: Dataset and Tools for Benchmarking Intrusion Detection in Internet of Medical Things. IEEE Access, 12, 115370–115385[10.1109/access.2024.3437214]

15.Fadwa Alrowais, Heba G. Mohamed, Fahd N. Al‐Wesabi, Mesfer Al Duhayyim, Anwer Mustafa Hilal, Abdelwahed Motwakel. (2023). Cyber attack detection in healthcare data using cyber-physical system with optimized algorithm. Computers & Electrical Engineering, 108, 108636[10.1016/j.compeleceng.2023.108636]

16.Bayu Adhi Tama, Kyung-Hyune Rhee. (2017). Attack Classification Analysis of IoT Network via Deep Learning Approach. Research Briefs on Information and Communication Technology Evolution, 3, 150–158[10.56801/rebicte.v3i.54]

17.Ling Li, Huawen Liu, Zongjie Ma, Yuchang Mo, Zhengjie Duan, Jiaqing Zhou, Jianmin Zhao. (2014). Multi-label Feature Selection via Information Gain. Lecture notes in computer science, 345–355[10.1007/978-3-319-14717-8_27]

18.Abdul Hanan Khan Mohammed, Hrag Jebamikyous, Dina Nawara, Rasha Kashef. (2021). IoT Cyber-Attack Detection: A Comparative Analysis. , 117–123[10.1145/3460620.3460742]

19.Khorshed Alam, Md Fahad Monir, Zoheb Hassan, Md. Tarek Habib. (2024). Optimizing IoT Network Intrusion Detection: A Deep Learning Approach. , 1–5[10.1109/ciot63799.2024.10757105]

20.E. B. Becker, Maanak Gupta, Kshitiz Aryal. (2023). Using Machine Learning for Detection and Classification of Cyber Attacks in Edge IoT. , 400–410[10.1109/edge60047.2023.00063]

21.Sina Pournouri, Shahrzad Zargari, Babak Akhgar. (2019). An Investigation of Using Classification Techniques in Prediction of Type of Targets in Cyber Attacks. , 202–212[10.1109/icgs3.2019.8688266]

22.(2008). Wiley Series in Probability and Statistics. Wiley series in probability and statistics[10.1002/series1345]

23.(2024). Random forests. Physics Subject Headings (PhySH)[10.29172/7c2a6982-6d72-4cd8-bba6-2fccb06a7011]