{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T13:35:20Z","timestamp":1770730520266,"version":"3.49.0"},"reference-count":36,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T00:00:00Z","timestamp":1754438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling continuous patient monitoring, early diagnosis, and personalized treatments. However, the het-erogeneity of IoMT devices and the lack of standardized protocols introduce serious security vulnerabilities. To address these challenges, we propose a hybrid BiLSTM-DNN intrusion detection system, named HBiLD-IDS, that combines Bidirectional Long Short-Term Memory (BiLSTM) networks with Deep Neural Networks (DNNs), leveraging both temporal dependencies in network traffic and hierarchical feature extraction. The model is trained and evaluated on the CICIoMT2024 dataset, which accurately reflects the diversity of devices and attack vectors encountered in connected healthcare environments. The dataset undergoes rigorous preprocessing, including data cleaning, feature selection through correlation analysis and recursive elimination, and feature normalization. Compared to existing IDS models, our approach significantly enhances detection accuracy and generalization capacity in the face of complex and evolving attack patterns. Experimental results show that the proposed IDS model achieves a classification accuracy of 98.81% across 19 attack types confirming its robustness and scalability. This approach represents a promising solution for strengthening the security posture of IoMT networks against emerging cyber threats.<\/jats:p>","DOI":"10.3390\/info16080669","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T07:45:11Z","timestamp":1754466311000},"page":"669","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["HBiLD-IDS: An Efficient Hybrid BiLSTM-DNN Model for Real-Time Intrusion Detection in IoMT Networks"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0089-877X","authenticated-orcid":false,"given":"Hamed","family":"Benahmed","sequence":"first","affiliation":[{"name":"LRIT Laboratory, Department of Computer Science, Faculty of Science, University of Abou Bekr Belka\u00efd, Tlemcen 13000, Algeria"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7899-2185","authenticated-orcid":false,"given":"Mohammed","family":"M\u2019hamedi","sequence":"additional","affiliation":[{"name":"LRIT Laboratory, Department of Computer Science, Faculty of Science, University of Abou Bekr Belka\u00efd, Tlemcen 13000, Algeria"},{"name":"Ecole Sup\u00e9rieure en Sciences Appliqu\u00e9es de Tlemcen, University of Abou Bekr Belka\u00efd, BP 165 RP Bel Horizon, Tlemcen 13000, Algeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0426-1668","authenticated-orcid":false,"given":"Mohammed","family":"Merzoug","sequence":"additional","affiliation":[{"name":"LRIT Laboratory, Department of Computer Science, Faculty of Science, University of Abou Bekr Belka\u00efd, Tlemcen 13000, Algeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6554-3925","authenticated-orcid":false,"given":"Mourad","family":"Hadjila","sequence":"additional","affiliation":[{"name":"STIC Laboratory, Department of Telecommunication, Faculty of Technology, University of Abou Bekr Belka\u00efd, Tlemcen 13000, Algeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1987-9065","authenticated-orcid":false,"given":"Amina","family":"Bekkouche","sequence":"additional","affiliation":[{"name":"LRIT Laboratory, Department of Computer Science, Faculty of Science, University of Abou Bekr Belka\u00efd, Tlemcen 13000, Algeria"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0790-3022","authenticated-orcid":false,"given":"Abdelhak","family":"Etchiali","sequence":"additional","affiliation":[{"name":"LRIT Laboratory, Department of Computer Science, Faculty of Science, University of Abou Bekr Belka\u00efd, Tlemcen 13000, Algeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8272-9425","authenticated-orcid":false,"given":"Sa\u00efd","family":"Mahmoudi","sequence":"additional","affiliation":[{"name":"Computer Science Department, University of Mons, 7000 Mons, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,6]]},"reference":[{"key":"ref_1","first-page":"3","article-title":"The Internet of Things in Healthcare: An Overview","volume":"1","author":"Yin","year":"2016","journal-title":"J. Ind. Inf. Integr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.jnca.2018.02.008","article-title":"The Role of Information and Communication Technologies in Healthcare: Taxonomies, Perspectives, and Challenges","volume":"107","author":"Aceto","year":"2018","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1007\/s11276-024-03764-8","article-title":"The Internet of Medical Things (IoMT): Opportunities and Challenges","volume":"31","author":"Sheikh","year":"2025","journal-title":"Wirel. Netw."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e4049","DOI":"10.1002\/ett.4049","article-title":"A Survey on Security Threats and Countermeasures in Internet of Medical Things (IoMT)","volume":"33","author":"Papaioannou","year":"2022","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Villegas-Ch, W., Govea, J., and Jaramillo-Alcazar, A. (2023). Tamper Detection in Industrial Sensors: An Approach Based on Anomaly Detection. Sensors, 23.","DOI":"10.3390\/s23218908"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tariq, U., Ullah, I., Yousuf Uddin, M., and Kwon, S.J. (2022). An Effective Self-Configurable Ransomware Prevention Technique for IoMT. Sensors, 22.","DOI":"10.3390\/s22218516"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1007\/s42452-024-06351-w","article-title":"IoMT Landscape: Navigating Current Challenges and Pioneering Future Research Trends","volume":"7","author":"Alturki","year":"2025","journal-title":"Discov. Appl. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cao, Y., Zhang, L., Zhao, X., Jin, K., and Chen, Z. (2022). An Intrusion Detection Method for Industrial Control System Based on Machine Learning. Information, 13.","DOI":"10.3390\/info13070322"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1007\/s10462-024-11101-w","article-title":"A Comprehensive and Systematic Literature Review on Intrusion Detection Systems in the Internet of Medical Things: Current Status, Challenges, and Opportunities","volume":"58","author":"Naghib","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1016\/j.scs.2017.12.041","article-title":"A Systematic Review of Data Protection and Privacy Preservation Schemes for Smart Grid Communications","volume":"38","author":"Ferrag","year":"2018","journal-title":"Sustain. Cities Soc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/MSP.2018.2825478","article-title":"IoT Security Techniques Based on Machine Learning: How Do IoT Devices Use AI to Enhance Security?","volume":"35","author":"Xiao","year":"2018","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1016\/j.future.2019.12.028","article-title":"Securing Internet of Medical Things Systems: Limitations, Issues and Recommendations","volume":"105","author":"Yaacoub","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2212","DOI":"10.1109\/TCAD.2018.2857280","article-title":"A Design Space Exploration Framework for Convolutional Neural Networks Implemented on Edge Devices","volume":"37","author":"Tsimpourlas","year":"2018","journal-title":"IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Genuario, F., Santoro, G., Giliberti, M., Bello, S., Zazzera, E., and Impedovo, D. (2024). Machine Learning-Based Methodologies for Cyber-Attacks and Network Traffic Monitoring: A Review and Insights. Information, 15.","DOI":"10.20944\/preprints202407.0029.v1"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7199","DOI":"10.1007\/s10586-024-04338-1","article-title":"An Industrial Network Intrusion Detection Algorithm Based on IGWO-GRU","volume":"27","author":"Yang","year":"2024","journal-title":"Cluster Comput."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, Y., Guo, X., Liu, Z., Zhang, X., and Liang, K. (2022). A BiLSTM-Based DDoS Attack Detection Method for Edge Computing. Energies, 15.","DOI":"10.3390\/en15217882"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Feng, W., Tang, S., Wang, S., He, Y., Chen, D., Yang, Q., and Fu, S. (2025). Characterizing Perception Deep Learning Algorithms and Applications for Vehicular Edge Computing. Algorithms, 18.","DOI":"10.3390\/a18010031"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Shaikh, J.A., Wang, C., Sima, M.W.U., Arshad, M., Owais, M., Hassan, D.S.M., Alkanhel, R., and Muthanna, M.S.A. (2025). A Deep Reinforcement Learning-Based Robust Intrusion Detection System for Securing IoMT Healthcare Networks. Front. Med., 12.","DOI":"10.3389\/fmed.2025.1524286"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5783","DOI":"10.1007\/s10115-025-02402-9","article-title":"Multi-Attention DeepCRNN: An Efficient and Explainable Intrusion Detection Framework for Internet of Medical Things Environments","volume":"67","author":"Sharma","year":"2025","journal-title":"Knowl. Inf. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"7002","DOI":"10.1109\/ACCESS.2025.3526883","article-title":"L2D2: A Novel LSTM Model for Multi-Class Intrusion Detection Systems in the Era of IoMT","volume":"13","author":"Akar","year":"2025","journal-title":"IEEE Access"},{"key":"ref_21","first-page":"2185","article-title":"Augmenting Internet of Medical Things Security: Deep Ensemble Integration and Methodological Fusion","volume":"141","author":"Naeem","year":"2024","journal-title":"Comput. Model. Eng. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Tseng, S.M., Wang, Y.Q., and Wang, Y.C. (2024). Multi-Class Intrusion Detection Based on Transformer for IoT Networks Using CIC-IoT-2023 Dataset. Future Internet, 16.","DOI":"10.3390\/fi16080284"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1007\/s42452-025-07071-5","article-title":"An Explainable AI-Driven Transformer Model for Spoofing Attack Detection in Internet of Medical Things (IoMT) Networks","volume":"7","author":"Alsharaiah","year":"2025","journal-title":"Discov. Appl. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Faruqui, N., Abu Yousuf, M., Azad, A., Alyami, S.A., Li\u00f2, P., Kabir, M.A., and Moni, M.A. (2023). SafetyMed: A Novel IoMT Intrusion Detection System Using CNN-LSTM Hybridization. Electronics, 12.","DOI":"10.3390\/electronics12173541"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Gueriani, A., Kheddar, H., and Mazari, A.C. (2024, January 24\u201325). Enhancing IoT Security with CNN and LSTM-Based Intrusion Detection Systems. Proceedings of the 2024 6th International Conference on Pattern Analysis and Intelligent Systems (PAIS), El Oued, Algeria.","DOI":"10.1109\/PAIS62114.2024.10541178"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sayegh, H.R., Dong, W., and Al-Madani, A.M. (2024). Enhanced Intrusion Detection with LSTM-Based Model, Feature Selection, and SMOTE for Imbalanced Data. Appl. Sci., 14.","DOI":"10.3390\/app14020479"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"28","DOI":"10.55056\/jec.648","article-title":"A Long Short-Term Memory Based Approach for Detecting Cyber Attacks in IoT Using CIC-IoT-2023 Dataset","volume":"3","author":"Jony","year":"2024","journal-title":"J. Edge Comput."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lipsa, S., Dash, R.K., and Ivkovi\u0107, N. (2025). An Interpretable Dimensional Reduction Technique with an Explainable Model for Detecting Attacks in Internet of Medical Things Devices. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-93404-8"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1186\/s40537-024-00886-w","article-title":"Machine Learning-Based Network Intrusion Detection for Big and Imbalanced Data Using Oversampling, Stacking Feature Embedding, and Feature Extraction","volume":"11","author":"Talukder","year":"2024","journal-title":"J. Big Data"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"112189","DOI":"10.1109\/ACCESS.2023.3318866","article-title":"A Novel Federated Edge Learning Approach for Detecting Cyberattacks in IoT Infrastructures","volume":"11","author":"Abbas","year":"2023","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"101631","DOI":"10.1016\/j.iot.2025.101631","article-title":"Evaluating and Enhancing Intrusion Detection Systems in IoMT: The Importance of Domain-Specific Datasets","volume":"32","author":"Siddiqui","year":"2025","journal-title":"Internet Things"},{"key":"ref_32","first-page":"188","article-title":"A Novel Data Preprocessing Model for Lightweight Sensory IoT Intrusion Detection","volume":"9","author":"Khanday","year":"2024","journal-title":"Int. J. Math. Eng. Manag. Sci."},{"key":"ref_33","first-page":"136","article-title":"Intrusion Detection System for Internet of Medical Things Using GRU with Attention Mechanism-Based Hybrid Deep Learning Technique","volume":"11","author":"Saran","year":"2024","journal-title":"Jordanian J. Comput. Inf. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e2751","DOI":"10.7717\/peerj-cs.2751","article-title":"Federated Learning with LSTM for Intrusion Detection in IoT-Based Wireless Sensor Networks: A Multi-Dataset Analysis","volume":"11","author":"Anwar","year":"2025","journal-title":"PeerJ Comput. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"101351","DOI":"10.1016\/j.iot.2024.101351","article-title":"CICIoMT2024: A Benchmark Dataset for Multi-Protocol Security Assessment in IoMT","volume":"28","author":"Dadkhah","year":"2024","journal-title":"Internet Things"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Tany, N.S., Suresh, S., Sinha, D.N., Shinde, C., Stolojescu-Crisan, C., and Khondoker, R. (2022). Cybersecurity Comparison of Brain-Based Automotive Electrical and Electronic Architectures. 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