An Intrusion Detection Model Using Machine Learning for Safeguarding Internet of Things Infrastructure Against Cyber Threats
DOI:
https://doi.org/10.62054/ijdm/0203.22Abstract
The Internet of Things (IoT) has become a critical enabler of modern digital services, yet its rapid growth has exposed billions of devices to cyber threats such as denial-of-service (DoS), distributed denial-of-service (DDoS), malware, and man-in-the-middle attacks. This study develops a machine learning-based Intrusion Detection System (IDS) tailored for IoT infrastructure security. Three benchmark datasets—BoT-IoT, IoT Healthcare, and TON-IoT—were preprocessed through exploratory data analysis, feature selection using ANOVA and Logistic Regression, and dimensionality reduction via PCA. Four models were implemented and optimized: Random Forest (RF), XGBoost, Recurrent Neural Network (RNN), and Gaussian Naive Bayes (GNB). Evaluation metrics included accuracy, precision, recall, and F1-score, with datasets split 70-15-15 for training, validation, and testing. Results indicate that RF consistently achieved the best accuracy (93–94%) across datasets, while XGBoost delivered comparable performance with shorter training time. RNN showed moderate performance, and GNB lagged due to its simplifying assumptions. The findings highlight that robust, scalable IDS solutions can be developed for IoT ecosystems, ensuring confidentiality, integrity, and availability
Riferimenti bibliografici
Ahanger, T.A., Tariq, U., Dahan, F., Chaudhry, S.A. & Malik, Y. (2023). Securing IoT Devices Running PureOS from Ransomware Attacks: Leveraging Hybrid Machine Learning Techniques. Mathematics, 11, 2481. https://doi.org/10.3390/math11112481
Alzahrani, M. A., & Siddiqui, M. S. (2022). Detecting DDoS Attacks in IoT-Based Networks Using Matrix Profile. Applied Sciences, 12, 8294. https://doi.org/10.3390/app12168294
Brownlee, J. (2019). How to choose a feature selection method for machine learning. Machine Learning Mastery. IEEE Communications Surveys & Tutorials, 21(2), 1636–1675.
Diro, A., & Chilamkurti, N. (2018). Leveraging LSTM networks for attack detection in fog-to-things communications. IEEE Communications Magazine, 56(9), 124–130.
Dissanayake, K., & Md Johar, M. G. (2021). Comparative study on heart disease prediction using feature selection techniques on classification algorithms. Applied Computational Intelligence and Soft Computing, 2021, 1-17.
Faisal H., Syed A., Ghalib A. Shah, Ivan P., Ubaid U., Farrukh S., Nuno M., Eftim Z. (2019). IoT Healthcare Security Dataset. IEEE Dataport. https://dx.doi.org/10.21227/9w13-2t13
Fryer, D., Strumke, I., & Nguyen, H. (2021). Shapley values for feature selection: The good, the bad, and the axioms. IEEE Access, 9, 144352-144360. https://doi.org/10.1109/access.2021.3119110
Haitham A. N., & Osama M. F. Abu-Sharkh (2023). "Code Injection Attacks in Wireless-Based Internet of Things (IoT): A Comprehensive Review and Practical Implementations." Computer Engineering Department, King Abdullah II School of Engineering, Princess Sumaya University for Technology, Amman 11941, Jordan, osama@psut.edu.jo Correspondence: h.ani@psut.edu.jo, Tel.: +962-6-535-9949 (ext. 5553)
Huda, S., Abawajy, J., Abdollahian, M., Islam, R., & Yearwood, J. (2017). A fast malware feature selection approach using a hybrid of multi‐linear and stepwise binary logistic regression. Concurrency and Computation: Practice and Experience, 29(23), e3912.
Koirala, A., Bista, R., & Ferreira, J. C. (2023). Enhancing IoT Device Security through Network Attack Data Analysis Using Machine Learning Algorithms. Future Internet, 15, 210. https://doi.org/10.3390/fi15060210
Le, K.-H., Nguyen, M.-H. Tran, T.-D., & Tran, N. D. (2022). IMIDS: An Intelligent Intrusion Detection System against Cyber Threats in IoT. Electronics, 11, 524. https://doi.org/10.3390/electronics11040524
Mrabet, H., Belguith, S., Alhomoud, A., & Jemai, A. (2020). A Survey of IoT Security Based on a Layered Architecture of Sensing and Data Analysis. Sensors, 20, 3625.
Siwakoti, Y. R., Bhurtel, M., Rawat, D. B., Oest, A., & Johnson, R. (2023). Advances in IoT security: Vulnerabilities, enabled Criminal Services, attacks and countermeasures. IEEE Internet of Things Journal, 10, 11224–11239.
Subrato, B., & Prajoy, P. (2022). Secure access and authorization model for IoT devices using blockchain technology. In Proceedings of the 2018 International Conference on Smart Technology for Smart Nation (SmartTechCon) (pp. 1203–1206). Bangalore, India.
UNSW: Cross-site scripting (XSS) vulnerability in Drupal 8.x before 8.1.10 allows remote attackers to inject arbitrary web script. http://www.cvedetails.com/cve/CVE-2016-7571/. Accessed July 19, 2021.
Dowloads
Pubblicato
Fascicolo
Sezione
Licenza
Copyright (c) 2025 International Journal of Development Mathematics (IJDM)
Questo volume è pubblicato con la licenza Creative Commons Attribuzione 4.0 Internazionale.
Authors are solely responsible for obtaining permission to reproduce any copyrighted material contained in the manuscript as submitted. Any instance of possible prior publication in any form must be disclosed at the time the manuscript is submitted and a
copy or link to the publication must be provided.
The Journal articles are open access and are distributed under the terms of the Creative
Commons Attribution-NonCommercial-NoDerivs 4.0 IGO License, which permits use,
distribution, and reproduction in any medium, provided the original work is properly cited.
No modifications or commercial use of the articles are permitted.
