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All issues Volume 86 (2026) ITM Web Conf., 86 (2026) 03003 References
Open Access
Issue
ITM Web Conf.
Volume 86, 2026
5th International Conference on Current Research in Engineering and Technology (ICCRET-2026)
Article Number 03003
Number of page(s) 10
Section IoT & Smart Systems
DOI https://doi.org/10.1051/itmconf/20268603003
Published online 05 June 2026
  1. Singh, N. Joychandra, et al. “Botnet-based IoT network traffic analysis using deep learn-ing.” Security and Privacy 7.2 (2024): e355. [Google Scholar]
  2. Chataut, Robin, Alex Phoummalayvane, and Robert Akl. “Unleashing the power of IoT: A comprehensive review of IoT applications and future prospects in healthcare, agriculture, smart homes, smart cities, and industry 4.0.” Sensors 23.16 (2023): 7194. [Google Scholar]
  3. Wu, Ju-Yu, et al. “IoT-based wearable health monitoring device and its validation for potential critical and emergency applications.” Frontiers in Public Health 11 (2023): 1188304. [Google Scholar]
  4. Ejiyi, Chukwuebuka, et al. “The internet of medical things in healthcare management: a review.” Journal of Digital Health (2023): 30–62. [Google Scholar]
  5. Nissar, Gousia, et al. “IoT in healthcare: a review of services, applications, key tech-nologies, security concerns, and emerging trends.” Multimedia Tools and Applications (2024): 1–62. [Google Scholar]
  6. Huang, Chenxi, et al. “Internet of medical things: A systematic review.” Neurocomputing (2023): 126719. [Google Scholar]
  7. Ksibi, Sondes, Faouzi Jaidi, and Adel Bouhoula. “IoMT security model based on machine learning and risk assessment techniques.” 2023 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2023. [Google Scholar]
  8. Rasool, Raihan Ur, et al. “Security and privacy of internet of medical things: A con-temporary review in the age of surveillance, botnets, and adversarial ML.” Journal of Network and Computer Applications 201 (2022): 103332. [Google Scholar]
  9. Kumar, A. Karthick, K. Vadivukkarasi, and R. Dayana. “A Novel Hybrid Deep Learning Model for Botnet Attacks Detection in a Secure IoMT Environment.” 2023 International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS). IEEE, 2023. [Google Scholar]
  10. Dadkhah, Sajjad, et al. “CICIoMT2024: Attack Vectors in Healthcare devices-A Multi-Protocol Dataset for Assessing IoMT Device Security.” Raphael and Chukwuka Molokwu, Reginald and Sadeghi, Somayeh and Ghorbani, Ali, CiCIoMT2024: Attack Vectors in Healthcare Devices-A Multi-Protocol Dataset for Assessing IoMT Device Security (2024). [Google Scholar]
  11. Hernandez-Jaimes, Mireya Lucia, et al. “Artificial intelligence for IoMT security: A review of intrusion detection systems, attacks, datasets and Cloud-Fog-Edge architec-tures.” Internet of Things (2023): 100887. [Google Scholar]
  12. Sowmya, T., and EA Mary Anita. “A comprehensive review of AI based intrusion de-tection system.” Measurement: Sensors 28 (2023): 100827. [Google Scholar]
  13. Singh, Nongthombam Joychandra, et al. “Massive IoT network traffic analysis using ML and DL methods: an empirical evaluation: N. Joychandra Singh et al.” The Journal of Supercomputing 81.10 (2025): 1107. [Google Scholar]
  14. Neto, Euclides Carlos Pinto, et al. “A review of Machine Learning (ML)-based IoT security in healthcare: A dataset perspective.” Computer Communications (2023). [Google Scholar]
  15. Hussain, Faisal, et al. “A framework for malicious traffic detection in IoT healthcare environment.” Sensors 21.9 (2021): 3025. [Google Scholar]
  16. Zubair, Mohammed, et al. “Secure Bluetooth communication in smart healthcare sys-tems: a novel community dataset and intrusion detection system.” Sensors 22.21 (2022): 8280. [Google Scholar]
  17. Ahmed, Mohiuddin, et al. “ECU-IoHT: A dataset for analyzing cyberattacks in Internet of Health Things.” Ad Hoc Networks 122 (2021): 102621. [Google Scholar]
  18. Chaganti, Rajasekhar, et al. “A particle swarm optimization and deep learning ap-proach for intrusion detection system in internet of medical things.” Sustainability 14.19 (2022): 12828. [Google Scholar]
  19. Ravi, Vinayakumar, Tuan D. Pham, and Mamoun Alazab. “Deep learning-based network intrusion detection system for Internet of medical things.” IEEE internet of things magazine 6.2 (2023): 50–54. [Google Scholar]
  20. Radoglou-Grammatikis, Panagiotis, et al. “Modeling, detecting, and mitigating threats against industrial healthcare systems: a combined software defined networking and rein-forcement learning approach.” IEEE Transactions on Industrial Informatics 18.3 (2021): 2041–2052. [Google Scholar]
  21. Faruqui, Nuruzzaman, et al. “SafetyMed: a novel IoMT intrusion detection system using CNN-LSTM hybridization.” Electronics 12.17 (2023): 3541. [Google Scholar]
  22. Umamaheswaran, S., et al. “Smart intrusion detection system with balanced data in IoMT infra.” Journal of Intelligent & Fuzzy Systems Preprint (2024): 1–17. [Google Scholar]
  23. Aguru, Aswani Devi, and Suresh Babu Erukala. “A lightweight multi-vector DDoS de-tection framework for IoT-enabled mobile health informatics systems using deep learn-ing.” Information Sciences 662 (2024): 120209. [Google Scholar]
  24. Alzubi, Jafar A., et al. “A blended deep learning intrusion detection framework for consumable edge-centric iomt industry.” IEEE Transactions on Consumer Electronics (2024). [Google Scholar]
  25. Sun, Zhenyang, et al. “Optimized machine learning enabled intrusion detection 2 system for internet of medical things.” Franklin Open 6 (2024): 100056. [Google Scholar]
  26. Akram, Faiza, et al. “Trustworthy intrusion detection in e-healthcare systems.” Frontiers in public health 9 (2021): 788347. [Google Scholar]
  27. Gong, Youdi, et al. “A survey on dataset quality in machine learning.” Information and Software Technology 162 (2023): 107268. [Google Scholar]
  28. Khalid, Nazish, et al. “Privacy-preserving artificial intelligence in healthcare: Tech-niques and applications.” Computers in Biology and Medicine 158 (2023): 106848. [Google Scholar]
  29. Hady, Anar A., et al. “Intrusion detection system for healthcare systems using medical and network data: A comparison study.” IEEE Access 8 (2020): 106576–106584. [Google Scholar]
  30. Hoque, Nazrul, Dhruba K. Bhattacharyya, and Jugal K. Kalita. “MIFS-ND: A mutual information-based feature selection method.” Expert systems with applications 41.14 (2014): 6371–6385. [Google Scholar]
  31. Hoque, Nazrul, Mihir Singh, and Dhruba K. Bhattacharyya. “EFS-MI: an ensemble fea-ture selection method for classification: An ensemble feature selection method.” Com-plex & Intelligent Systems 4 (2018): 105–118. [Google Scholar]
  32. Shekhar, Shashank, Nazrul Hoque, and Dhruba K. Bhattacharyya. “PKNN-MIFS: a parallel KNN classifier over an optimal subset of features.” Intelligent systems with applications 14 (2022): 200073. [Google Scholar]
  33. Kingma, Diederik P., and Max Welling. “Auto-encoding variational bayes.” arXiv preprint arXiv:1312.6114 (2013). [Google Scholar]
  34. Singh, N. Joychandra, et al. “Significance of feature selection on IoT-based botnet at-tacks identification using machine learning.” 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. [Google Scholar]

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