2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 16 , Issue 1 , PP: , 2024 | Cite this article as | XML | Html | PDF
Sultan Almotairi 1 * , Deepak Dasaratha Rao 2 , Olayan Alharbi 3 , Zaid Alzaid 4 , Yasser M. Hausawi 5 , Jaber Almutairi 6
Doi: https://doi.org/10.54216/FPA.160112
Intrusion detection in the IoMT (Internet of Medical Things) represents the process of keeping track of and discovering unauthorized or malicious actions in medical devices and networks. Some of its benefits include early detection of potential threats, prevention of data breaches, and protection of patient privacy. Aside from these benefits, some difficulties are evident, like alarm fatigue due to false positives, the complexity in the standardizing detection across different devices, and resource limits that hinder qualitative implementations, thus leaving some vulnerabilities in the healthcare infrastructure. This paper proposes a new Efficient Intrusion Detection model based on the Correlation-Based Feature Selection and the OptCNN-LSTM model to address these problems. The proposed methodology comprises five key phases: (i) Data Acquisition (ii) Pre-processing (iii) Feature Extraction (iv) Feature Selection (v) OptCNN-LSTM Model-based intrusion detection. The raw data is first gathered and then preprocessed using z-score normalization and data cleaning. Then, the best features are extracted using central tendency, the degree of dispersion, and correlation. A mixed IHHO-PSO feature with the Correlation-based Feature Selection (CFS) framework is employed to choose the best features amongst the collected features. At last, the OptCNN-LSTM model is performed to detect the intrusion in the IoMT based on features-selected data. The CNN is tuned using the Levy Flight Optimization (LF) which can be further combined with the LSTM to get the expected results. The code is written in Python and the model is then run to determine its performance which is measured in terms of accuracy, precision, f-measure, and a Receiver Operating Characteristic Curve (ROC). Compared to the current models, the proposed model has the highest accuracies 97.6% and 96.5% for learning rates 70 and 80, respectively…
IoMT , Intrusion Detection , Correlation , Feature Selection , ROC , &hellip , .
[1] Gokhale, P., Bhat, O. and Bhat, S., 2018. Introduction to IOT. International Advanced Research Journal in Science, Engineering and Technology, 5(1), pp.41-44.
[2] Villegas-Ch, W.; García-Ortiz, J.; Urbina-Camacho, I. Framework for a Secure and Sustainable Internet of Medical Things, Requirements, Design Challenges, and Future Trends. Appl. Sci. 2023, 13, 6634. https://doi.org/10.3390/app13116634
[3] Das, P.K., Zhu, F., Chen, S., Luo, C., Ranjan, P. and Xiong, G., 2019, June. Smart medical healthcare of Internet of Medical Things (IOMT): Application of non-contact sensing. In 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) (pp. 375-380). IEEE.
[4] Javaid, A., Niyaz, Q., Sun, W. and Alam, M., 2016, May. A deep learning approach for network intrusion detection system. In Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS) (pp. 21-26).
[5] Hady, A.A., Ghubaish, A., Salman, T., Unal, D. and Jain, R., 2020. Intrusion detection system for healthcare systems using medical and network data: A comparison study. IEEE Access, 8, pp.106576-106584.
[6] Zachos, G., Essop, I., Mantas, G., Porfyrakis, K., Ribeiro, J.C. and Rodriguez, J., 2021. An anomaly-based intrusion detection system for Internet of Medical Things networks. Electronics, 10(21), p.2562.
[7] Binbusayyis, A., Alaskar, H., Vaiyapuri, T. and Dinesh, M., 2022. An investigation and comparison of machine learning approaches for intrusion detection in IoMT network. The Journal of Supercomputing, 78(15), pp.17403-17422.
[8] Mishra, P.; Singh, G. Internet of Medical Things Healthcare for Sustainable Smart Cities: Current Status and Future Prospects. Appl. Sci. 2023, 13, 8869. https://doi.org/10.3390/ app13158869
[9] Faruqui, N., Yousuf, M.A., Whaiduzzaman, M., Azad, A.K.M., Alyami, S.A., Liò, P., Kabir, M.A. and Moni, M.A., 2023. SafetyMed: a novel IoMT intrusion detection system using CNN-LSTM hybridization. Electronics, 12(17), p.3541.
[10] Chaganti, R., Mourade, A., Ravi, V., Vemprala, N., Dua, A. and Bhushan, B., 2022. A particle swarm optimization and deep learning approach for intrusion detection system in the internet of medical things. Sustainability, 14(19), p.12828.
[11] Singh, P., Gaba, G.S., Kaur, A., Hedabou, M. and Gurtov, A., 2022. Dew-cloud-based hierarchical federated learning for intrusion detection in IoMT. IEEE Journal of Biomedical and Health Informatics, 27(2), pp.722-731.
[12] Norouzi, M., Gürkaş-Aydın, Z., Turna, Ö.C., Yağci, M.Y., Aydin, M.A. and Souri, A., 2023. A Hybrid Genetic Algorithm-Based Random Forest Model for Intrusion Detection Approach in Internet of Medical Things. Applied Sciences, 13(20), p.11145.
[13] Ravi, V., Pham, T.D. and Alazab, M., 2023. Deep Learning-Based Network Intrusion Detection System for Internet of Medical Things. IEEE Internet of Things Magazine, 6(2), pp.50-54.
[14] Alalhareth, M. and Hong, S.C., 2023. An Adaptive Intrusion Detection System in the Internet of Medical Things Using Fuzzy-Based Learning. Sensors, 23(22), p.9247.
[15] Shambharkar, P.G. and Sharma, N., 2023. Artificial Intelligence-driven Intrusion Detection Framework for the Internet of Medical Things.
[16] RM, S.P., Maddikunta, P.K.R., Parimala, M., Koppu, S., Gadekallu, T.R., Chowdhary, C.L. and Alazab, M., 2020. An effective feature engineering for DNN using hybrid PCA-GWO for intrusion detection in IoMT architecture. Computer Communications, 160, pp.139-149.
[17] Hady, A.A., Ghubaish, A., Salman, T., Unal, D. and Jain, R., 2020. Intrusion detection system for healthcare systems using medical and network data: A comparison study. IEEE Access, 8, pp.106576-106584.
[18] Taouali, O., Bacha, S., Ben Abdellafou, K., Aljuhani, A., Zidi, K., Alanazi, R. and Harkat, M.F., 2023. Intelligent Intrusion Detection System for the Internet of Medical Things Based on Data-Driven Techniques. Computer Systems Science & Engineering, 47(2).
[19] Panda, S.K. and Jana, P.K., 2018. Normalization-based task scheduling algorithms for a heterogeneous multi-cloud environment. Information Systems Frontiers, 20, pp.373-399.
[20] Perumal, R. and Venkatachalam, S.B., 2023. Non-Invasive Decay Analysis of Monument Using Deep Learning Techniques. Traitement du Signal, 40(2).
[21] Sharma, A. and Suryawanshi, A., 2016. A novel method for detecting spam email using KNN classification with spearman correlation as distance measure. International Journal of Computer Applications, 136(6), pp.28-35.
