Abstract
Millennials have the advantage of accessing readily available modern scientific advancements, particularly in technology. One of these technologies that encompasses varied functionalities is the Internet of Things (IoT). In the midst of the Covid-19 pandemic, IoT, specifically Internet of Medical Things (IoMT), had pivotal significance in monitoring and tracking different health parameters. It autonomously manages an individual’s health data and stores the same as Electronic Health Records (EHRs). However, the networking protocols used by IoMT are not adequate enough to ensure the security and privacy of EHRs. Consequently, such technology is susceptible to cyber-attacks, which have become more prevalent over time and have taken various forms, that generally the stakeholders are not aware of. This paper introduces machine learning-driven intrusion detection systems as a solution to tackle this issue. The focus of this study is on devising a Machine Learning (ML) oriented Intrusion Detection System (IDS) designed to identify cyber-attacks targeting IoMT based systems. Several classification based ML techniques such as Multinomial Naive Bayes, Logistic Regression, Logistic Regression with Stochastic Gradient Descent, Linear Support Vector Classification, Decision Tree, Ensemble Voting Classifier, Bagging, Random Forest, Adaptive Boosting, Gradient Boosting and Extreme Gradient Boosting were used, whereupon the Adaptive Boosting was experimentally found to perform the best on performance metrics such as accuracy, precision, recall, F1-score, False Detection Rate (FDR) and False Positive Rate (FPR). Further, it was found that Adaptive boosting based IDS for IoMT performed comparatively better than the existing ToN_IoT based IDS models on performance metrics such as accuracy, F1-score, FPR and FDR.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abbas A, Khan MA, Latif S, Ajaz M, Shah AA, Ahmad J (2022) A new ensemble-based intrusion detection system for Internet of Things. Arab J Sci Eng 47(2):1805–1819. https://doi.org/10.1007/s13369-021-06086-5
Agarwal A, Khari M, Singh R (2021) Detection of DDOS attack using deep learning model in cloud storage application. Wireless Pers Commun 127:419–439. https://doi.org/10.1007/s11277-021-08271-z
Alabsi BA, Anbar M, Rihan SD (2023) Conditional tabular generative adversarial based intrusion detection system for detecting ddos and dos attacks on the Internet of Things networks. Sensors. https://doi.org/10.3390/s23125644
Aldhaheri S, Alghazzawi D, Cheng L, Alzahrani B, Al-Barakati A (2020) DeepDCA: novel network-based detection of iot attacks using artificial immune system. Appl Sci 10(6):85. https://doi.org/10.3390/app10061909
Alrashdi I, Alqazzaz A, Alharthi R, Aloufi E, Zohdy MA, Ming H (2019) FBAD: fog-based attack detection for IoT healthcare in smart cities. In 2019 IEEE 10th annual ubiquitous computing, electronics and mobile communication conference (UEMCON), pp 0515–0522. https://doi.org/10.1109/UEMCON47517.2019.8992963
Bottou L (2010) Large-scale machine learning with Stochastic gradient descent. In Y Lechevallier, G Saporta (Eds.) Proceedings of COMPSTAT’2010, pp 177–186. Heidelberg: Physica. https://doi.org/10.1007/978-3-7908-2604-3_16
Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140. https://doi.org/10.1007/BF00058655
Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324
Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 785–794. Presented at the San Francisco, California, USA. https://doi.org/10.1145/2939672.2939785
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297. https://doi.org/10.1007/BF00994018
Cox DR (1958) The regression analysis of binary sequences. J R Stat Soc Ser B 20(2):215–242
Freund Y, Schapire RE (1995). A desicion-theoretic generalization of on-line learning and an application to boosting. In P Vitányi (Ed.) Computational learning theory, pp 23–37. Berlin: Springer. https://doi.org/10.1007/3-540-59119-2_166
Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189–1232. https://doi.org/10.1214/aos/101320345
Gad AR, Nashat AA, Barkat TM (2021) Intrusion detection system using machine learning for vehicular Ad Hoc networks based on ToN-IoT dataset. IEEE Access 9:142206–142217. https://doi.org/10.1109/ACCESS.2021.3120626
Geron A (2019) Hands-on machine learning with scikit-learn, keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems, 2nd edn. O’Reilly Media, Berlin
IBM Security (2022) X-Force Threat Intelligence Index 2022. IBM Corporation. Retrieved from https://www.ibm.com/downloads/cas/ADLMYLAZ
Kandasamy K, Srinivas S, Achuthan K, Rangan VP (2022) Digital healthcare-cyberattacks in asian organizations: an analysis of vulnerabilities, risks, NIST perspectives, and recommendations. IEEE Access 10:12345–12364. https://doi.org/10.1109/ACCESS.2022.3145372
Khan NW, Alshehri MS, Khan MA, Almakdi S, Moradpoor N, Alazeb A, Ullah S, Naz N, Ahmad J (2023) A hybrid deep learning-based intrusion detection system for IoT networks. Math Biosci Eng 20(8):13491–13520
Kintzlinger M, Cohen A, Nissim N, Rav-Acha M, Khalameizer V, Elovici Y, Shahar Y, Katz A (2020) CardiWall: a trusted firewall for the detection of malicious clinical programming of cardiac implantable electronic devices. IEEE Access 8:48123–48140. https://doi.org/10.1109/ACCESS.2020.2978631
Kioskli K, Fotis T, Mouratidis H (2021) The landscape of cybersecurity vulnerabilities and challenges in healthcare: security standards and paradigm shift recommendations. In Ares 21: proceedings of the 16th international conference on availability, reliability and security. Vienna, Austria, pp 136. https://doi.org/10.1145/3465481.3470033
Kulkarni DD, Jaiswal RK (2023) An intrusion detection system using extended Kalman filter and neural networks for IoT networks. J Netw Syst Manage 31(3):56. https://doi.org/10.1007/s10922-023-09748-x
Kulshrestha P, Vijay Kumar TV, Khari M (2023) Intrusion detection system for internet of medical things. In International conference on advances in IoT and security with AI (ICAISA-2023), March 24–25, 2023, New Delhi
Kumar P, Gupta GP, Tripathi R (2021) An ensemble learning and fog-cloud architecture-driven cyber-attack detection framework for IoMT networks. Comput Commun 166:110–124. https://doi.org/10.1016/j.comcom.2020.12.003
Lee JD, Cha HS, Rathore S, Park JH (2021) M-IDM: a multi-classification based intrusion detection model in healthcare IoT. Comput Mater Contin 67(2):1537–1553
Littlestone N, Warmuth MK (1994) The weighted majority algorithm. Inf Comput 108(2):212–261. https://doi.org/10.1006/inco.1994.1009
Liu M, Xue Z, Xu X, Zhong C, Chen J (2018) Host-based intrusion detection system with system calls: review and future trends. ACM Comput Surv 51(5):52
McCallum A, Nigam K (1998) A comparison of event models for naive bayes text classification. In Learning for text categorization: papers from the 1998 AAAI Workshop, pp 41–48. Retrieved from http://www.kamalnigam.com/papers/multinomial-aaaiws98.pdf
Mehmood M, Javed T, Nebhen J, Abbas S, Abid R, Bojja GR, Rizwan M (2021) A hybrid approach for network intrusion detection. Comput Mater Contin 70(1):91–107
Moustafa N (2021) A new distributed architecture for evaluating AI-based security systems at the edge: network TON_IoT datasets. Sustain Cities Soc 72:102994. https://doi.org/10.1016/j.scs.2021.102994
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Mathieu B, Peter P, Ron W, Vincent D, Jake V, Alexandre P, David C, Matthieu B, Matthieu P, Duchesnay É (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12(85):2825–2830
Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106. https://doi.org/10.1007/BF00116251
Sarkar N, Keserwani PK, Govil MC (2023) A better and fast cloud intrusion detection system using improved squirrel search algorithm and modified deep belief network. Clust Comput 5:1573–7543. https://doi.org/10.1007/s10586-023-04037-3
Sharma A, Singh D (2020) Evolution of industrial revolutions: a review. Int J Innov Technol Explor Eng 9(11):2278–3075
Swarna Priya RM, Maddikunta PKR, Parimala M, Koppu S, Gadekallu TR, Chowdhary CL, Alazab M (2020) An effective feature engineering for DNN using hybrid PCA-GWO for intrusion detection in IoMT architecture. Comput Commun 160:139–149. https://doi.org/10.1016/j.comcom.2020.05.048
Zachos G, Essop I, Mantas G, Porfyrakis K, Ribeiro JC (2021) An anomaly-based intrusion detection system for internet of medical things networks. Electronics 10(21):2562. https://doi.org/10.3390/electronics10212562
Funding
None of the authors of the paper have received any funding for the work reported in this paper.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interests.
Human and animal rights
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Kulshrestha, P., Vijay Kumar, T.V. Machine learning based intrusion detection system for IoMT. Int J Syst Assur Eng Manag 15, 1802–1814 (2024). https://doi.org/10.1007/s13198-023-02119-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-023-02119-4