ARCHIVES
Original Article
An Enhanced Hybrid Machine Learning Model for Detecting DoS Attacks in IoT Network
Ngunyi Beatrice1
Dr. Muriuki David2
Dr. Andrew Anyembe3
1DCIST, The Cooperative University of Kenya, Kenya. 2DMS, The Cooperative University of Kenya, Kenya. 3DMS, Southern Eastern Kenya University, Kenya.
Published Online: September-December 2025
Pages: 134-139
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250403024
References
1. Abdulhammed, R., Abdullahi, M., Arif, F., & Anuar, N. B. (2019). A review on recent advances in intrusion detection systems using machine learning. Journal of Information Security and Applications, 44, 165–182.
2. Ali, A., & Rehman, A. (2022). A survey on machine learning approaches for IoT security. Journal of Network and Computer Applications, 200, 103312.
3. Bai, Y., Yang, D., & Xue, Y. (2022). Anomaly detection in IoT using Isolation Forest and unsupervised deep learning. IEEE Access, 10, 55689–55701.
4. Bai, Y., Zhuang, X., & Liu, Z. (2022). Lightweight anomaly detection for IoT with isolation forest optimization. Ad Hoc Networks, 132, 102891. https://doi.org/10.1016/j.adhoc.2022.102891
5. Chen, Z., Huang, H., & Zhou, X. (2021). Lightweight defense mechanisms against DoS attacks in IoT: A review. Computers & Security, 105, 102247.
6. Das, S., Debbarma, R., & Sharma, M. (2021). IoT protocol vulnerabilities and DoS attack countermeasures. International Journal of Communication Systems, 34(15), e4829.
7. García, S., Grill, M., Stiborek, J., & Zunino, A. (2020). An empirical comparison of botnet detection methods. Computers & Security, 87, 101568.
8. Ibrahim, M., Rahim, A., & Khalid, M. (2023). Recent trends in IoT botnets and distributed denial-of-service attacks. Future Internet, 15(2), 35.
9. Jin, J., Zhang, L., & Xu, X. (2022). A comparative analysis of machine learning models for DoS attack detection in smart IoT systems. Sensors, 22(9), 3211.
10. Jin, X., Yu, H., & Zhao, Q. (2022). Random forest-based intrusion detection for the Internet of Things. Computer Communications, 181, 50–60. https://doi.org/10.1016/j.comcom.2021.09.017
11. Kumar, R., Sharma, A., & Yadav, R. (2023). Comparative study of supervised classifiers for intrusion detection in IoT. Security and Privacy, 6(2), e251.
12. Kurniawan, A., & Handayani, P. (2021). Security issues and challenges in IoT architecture: A review. Indonesian Journal of Electrical Engineering and Computer Science, 21(1), 1–10.
13. Li, F., Zhang, X., & Jin, Y. (2020). A survey of network anomaly detection methods. Journal of Network and Computer Applications, 154, 102526.
14. Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2021). Deep learning for IoT big data and streaming analytics: A survey. IEEE Communications Surveys & Tutorials, 23(2), 1124-1168.
15. Nguyen, V., Hoang, D., & Pham, T. (2022). Hybrid machine learning model for IoT intrusion detection: A case of random and isolation forest integration. Computers, 11(9), 123.
16. Nguyen, T. D., Dinh, T. N., & Hoang, D. (2022). A hybrid ML approach for IoT security: Integrating supervised and unsupervised detection. Future Generation Computer Systems, 129, 91–102. https://doi.org/10.1016/j.future.2021.10.011
17. Nguyen, T. T., Pham, H. X., & Le, Q. (2022). A hybrid machine learning model for network intrusion detection. Journal of Information Security and Applications, 65, 103139.
18. Omar, A., Alsaeedi, A., & Rizwan, M. (2023). A layered hybrid intrusion detection system for smart environments. Sensors, 23(3), 1002.
19. Omar, M., Alshamrani, M., & Khan, M. A. (2023). A hybrid anomaly detection model for IoT security using machine learning and unsupervised clustering. Sensors, 23(2), 512.
20. Patel, H., Yadav, V., & Joshi, N. (2021). A hybrid approach using RF and IF for anomaly-based intrusion detection in IoT. Journal of Cybersecurity Technology, 5(4), 215–233.
21. Sahoo, S. R., & Padhy, N. (2021). Performance analysis of machine learning techniques in DoS attack detection for IoT. International Journal of Information Security Science, 10(2), 98-107.
22. Sharma, R., Singh, G., & Mahajan, R. (2023). Intrusion detection in IoT using ensemble models. Journal of Information Security and Applications, 71, 103470.
23. Sharma, A., Kapoor, R., & Singh, R. (2023). Threat detection in smart IoT systems using hybrid machine learning models. Journal of Information Security and Applications, 74, 103472. https://doi.org/10.1016/j.jisa.2023.103472
24. Zhang, Y., Liu, C., & Ma, J. (2023). Real-time anomaly detection in IoT using an enhanced isolation forest model. IEEE Internet of Things Journal, 10(6), 5021–5032
25. Zhang, W., Zhou, Y., & Wang, Y. (2023). Isolation-based anomaly detection in intelligent IoT networks. Computers & Security, 124, 102940. https://doi.org/10.1016/j.cose.2022.102940.
2. Ali, A., & Rehman, A. (2022). A survey on machine learning approaches for IoT security. Journal of Network and Computer Applications, 200, 103312.
3. Bai, Y., Yang, D., & Xue, Y. (2022). Anomaly detection in IoT using Isolation Forest and unsupervised deep learning. IEEE Access, 10, 55689–55701.
4. Bai, Y., Zhuang, X., & Liu, Z. (2022). Lightweight anomaly detection for IoT with isolation forest optimization. Ad Hoc Networks, 132, 102891. https://doi.org/10.1016/j.adhoc.2022.102891
5. Chen, Z., Huang, H., & Zhou, X. (2021). Lightweight defense mechanisms against DoS attacks in IoT: A review. Computers & Security, 105, 102247.
6. Das, S., Debbarma, R., & Sharma, M. (2021). IoT protocol vulnerabilities and DoS attack countermeasures. International Journal of Communication Systems, 34(15), e4829.
7. García, S., Grill, M., Stiborek, J., & Zunino, A. (2020). An empirical comparison of botnet detection methods. Computers & Security, 87, 101568.
8. Ibrahim, M., Rahim, A., & Khalid, M. (2023). Recent trends in IoT botnets and distributed denial-of-service attacks. Future Internet, 15(2), 35.
9. Jin, J., Zhang, L., & Xu, X. (2022). A comparative analysis of machine learning models for DoS attack detection in smart IoT systems. Sensors, 22(9), 3211.
10. Jin, X., Yu, H., & Zhao, Q. (2022). Random forest-based intrusion detection for the Internet of Things. Computer Communications, 181, 50–60. https://doi.org/10.1016/j.comcom.2021.09.017
11. Kumar, R., Sharma, A., & Yadav, R. (2023). Comparative study of supervised classifiers for intrusion detection in IoT. Security and Privacy, 6(2), e251.
12. Kurniawan, A., & Handayani, P. (2021). Security issues and challenges in IoT architecture: A review. Indonesian Journal of Electrical Engineering and Computer Science, 21(1), 1–10.
13. Li, F., Zhang, X., & Jin, Y. (2020). A survey of network anomaly detection methods. Journal of Network and Computer Applications, 154, 102526.
14. Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2021). Deep learning for IoT big data and streaming analytics: A survey. IEEE Communications Surveys & Tutorials, 23(2), 1124-1168.
15. Nguyen, V., Hoang, D., & Pham, T. (2022). Hybrid machine learning model for IoT intrusion detection: A case of random and isolation forest integration. Computers, 11(9), 123.
16. Nguyen, T. D., Dinh, T. N., & Hoang, D. (2022). A hybrid ML approach for IoT security: Integrating supervised and unsupervised detection. Future Generation Computer Systems, 129, 91–102. https://doi.org/10.1016/j.future.2021.10.011
17. Nguyen, T. T., Pham, H. X., & Le, Q. (2022). A hybrid machine learning model for network intrusion detection. Journal of Information Security and Applications, 65, 103139.
18. Omar, A., Alsaeedi, A., & Rizwan, M. (2023). A layered hybrid intrusion detection system for smart environments. Sensors, 23(3), 1002.
19. Omar, M., Alshamrani, M., & Khan, M. A. (2023). A hybrid anomaly detection model for IoT security using machine learning and unsupervised clustering. Sensors, 23(2), 512.
20. Patel, H., Yadav, V., & Joshi, N. (2021). A hybrid approach using RF and IF for anomaly-based intrusion detection in IoT. Journal of Cybersecurity Technology, 5(4), 215–233.
21. Sahoo, S. R., & Padhy, N. (2021). Performance analysis of machine learning techniques in DoS attack detection for IoT. International Journal of Information Security Science, 10(2), 98-107.
22. Sharma, R., Singh, G., & Mahajan, R. (2023). Intrusion detection in IoT using ensemble models. Journal of Information Security and Applications, 71, 103470.
23. Sharma, A., Kapoor, R., & Singh, R. (2023). Threat detection in smart IoT systems using hybrid machine learning models. Journal of Information Security and Applications, 74, 103472. https://doi.org/10.1016/j.jisa.2023.103472
24. Zhang, Y., Liu, C., & Ma, J. (2023). Real-time anomaly detection in IoT using an enhanced isolation forest model. IEEE Internet of Things Journal, 10(6), 5021–5032
25. Zhang, W., Zhou, Y., & Wang, Y. (2023). Isolation-based anomaly detection in intelligent IoT networks. Computers & Security, 124, 102940. https://doi.org/10.1016/j.cose.2022.102940.
Related Articles
2025
Transforming Cyber-Physical Systems: Machine Learning for Secure and Efficient Solutions
2025
Exploring AI Techniques for Quantum Threat Detection and Prevention
2025
Maturity Models for Business Intelligence: An Overview
2025
INSPIRO: An AI Driven Institution Auditor
2025
Adaptive AI Framework for Anomaly Detection and DDoS Mitigation in Distributed Systems
2025
