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FoSDeT: a new hybrid machine learning model for accurate and fast detection of IoT botnet

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Category: Content №1 2025

Last Updated on 25 February 2025

Published on 30 November -0001

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Authors:

I.Syamsuddin*, orcid.org/0000-0002-6017-7364, Politeknik Negeri Ujung Pandang, Makassar, Indonesia, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

D.Al-Dabass, orcid.org/0009-0001-7312-4712, Nottingham Trent University, Nottingham, the United Kingdom

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2025, (1): 104 - 109

https://doi.org/10.33271/nvngu/2025-1/104

Abstract:

Purpose. This study is aimed at introducing a new hybrid machine learning model to enhance the accuracy and speed in detecting botnet attacks in Internet of Things networks. The new model is derived from an integration of decision tree algorithm and feature selection algorithms to produce a novel hybrid machine learning for better performance in IoT botnet detection.

Methodology. The study adopts a six steps research methodology. It consists of dataset collection, dataset preprocessing, applying machine learning, comparing feature selection algorithms, combining both machine learning and feature selection algorithms, and finally comparing the results.

Findings. A novel hybrid machine learning (ML) model called FoSDeT has been obtained as a result of combination of decision tree algorithm and feature selection algorithm called Forward Selection which shows a significant improvement in IoT botnet detection in comparison to standard decision tree model.

Originality. The paper proposes a simple yet powerful hybrid approach which integrates Decision Tree algorithm with two pre-defined feature selection algorithms namely, Forward Selection and Backward Elimination. The new hybrid model called FoSDeT shows a significant enhancement in terms of IoT botnet detection.

Practical value. The hybrid model obtained from this study might be used by IT security practitioners in developing real intrusion detection system for defending IoT networks from botnet attacks.

Keywords: IoT, botnet, cyber attack, machine learning, detection accuracy, detection speed

References.

1. Ozmen, M. O., Song, R., Farrukh, H., & Celik, Z. B. (2023, January). Evasion attacks and defenses on smart home physical event verification. Network and Distributed System Security Symposium (NDSS). Internet Society. https://doi.org/10.48550/arXiv.2401.08141.

2. Sadeghi-Niaraki, A. (2023). Internet of Thing (IoT) review of review: Bibliometric overview since its foundation. Future Generation Computer Systems, 143, 361-377. https://doi.org/10.1016/j.future.2023.01.016.

3. Almazrouei, O. S. M. B. H., Magalingam, P., Hasan, M. K., & Shanmugam, M. (2023). A review on attack graph analysis for iot vulnerability assessment: challenges, open issues, and future directions. IEEE Access, 11, 44350-44376. https://doi.org/10.1109/ACCESS.2023.3272053.

4. Meidan, Y., Bohadana, M., Shabtai, A., Guarnizo, J. D., Ochoa, M., Tippenhauer, N. O., & Elovici, Y. (2017, April). ProfilIoT: A machine learning approach for IoT device identification based on network traffic analysis. Proceedings of the symposium on applied computing, (pp. 506-509). https://doi.org/10.1145/3019612.301987.

5. Zhao, H., Shu, H., & Xing, Y. (2021, January). A review on IoT botnet. The 2 ^nd International Conference on Computing and Data Science, (pp. 1-7). https://doi.org/10.1145/3448734.34509.

6. Razdan, S., Gupta, H., & Seth, A. (2021, April). Performance analysis of network intrusion detection systems using j48 and naive bayes algorithms. 2021 6 ^th International Conference for Convergence in Technology (I2CT), (pp. 1-7). IEEE. https://doi.org/10.1109/I2CT51068.2021.9417971.

7. Kotak, J., & Elovici, Y. (2023). IoT device identification based on network communication analysis using deep learning. Journal of Ambient Intelligence and Humanized Computing, 14(7), 9113-9129. https://doi.org/10.1007/s12652-022-04415-6.

8. Syamsuddin, I., Nur, R., Olivya, M., Irmawati, & Saharuna, Z. (2020). Evaluation of a Novel Intelligent Firewall Simulator for Dynamic Cyber Attack Lab. Artificial Intelligence and Bioinspired Computational Methods: Proceedings of the 9 ^th Computer Science On-line Conference 2020, 29, (pp. 257-267). Springer International Publishing.

9. Zebari, R., Abdulazeez, A., Zeebaree, D., Zebari, D., & Saeed, J. (2020). A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction. Journal of Applied Science and Technology Trends, 1(1), 56-70. https://doi.org/10.38094/jastt1224.

10. Beigi, E. B., Jazi, H. H., Stakhanova, N., & Ghorbani, A. A. (2014, October). Towards effective feature selection in machine learning-based botnet detection approaches. 2014 IEEE Conference on Communications and Network Security, (pp. 247-255). IEEE. https://doi.org/10.1109/CNS.2014.6997492.

11. Singh, K., Guntuku, S. C., Thakur, A., & Hota, C. (2014). Big data analytics framework for peer-to-peer botnet detection using random forests. Information Sciences, 278, 488-497. https://doi.org/10.1016/j.ins.2014.03.066.

12. Alejandre, F. V., Cortés, N. C., & Anaya, E. A. (2017, February). Feature selection to detect botnets using machine learning algorithms. 2017 international conference on electronics, communications and computers (CONIELECOMP), (pp. 1-7). IEEE. https://doi.org/10.1109/CONIELECOMP.2017.7891834

13. Miller, S., & Busby-Earle, C. (2016, December). The role of machine learning in botnet detection. 2016 11^th international conference for internet technology and secured transactions (ICITST), (pp. 359-364). IEEE. https://doi.org/10.1109/ICITST.2016.7856730.

14. Pektaş, A., & Acarman, T. (2017, July). Effective feature selection for botnet detection based on network flow analysis. International Conference Automatics and Informatics, (pp. 1-4).

15. Gadelrab, M. S., ElSheikh, M., Ghoneim, M. A., & Rashwan, M. (2018). BotCap: Machine learning approach for botnet detection based on statistical features. International Journal of Communication Networks and Information Security, 10(3), 563.

16. Hoang, X. D., & Nguyen, Q. C. (2018). Botnet detection based on machine learning techniques using DNS query data. Future Internet, 10(5), 43. https://doi.org/10.3390/fi10050043.

17. Mathur, L., Raheja, M., & Ahlawat, P. (2018). Botnet detection via mining of network traffic flow. Procedia computer science, 132, 1668-1677. https://doi.org/10.1016/j.procs.2018.05.137.

18. Khraisat, A., Gondal, I., Vamplew, P., & Kamruzzaman, J. (2019). Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity, 2(1), 1-22. https://doi.org/10.1186/s42400-019-0038-7.

19. Nõmm, S., & Bahşi, H. (2018, December). Unsupervised anomaly based botnet detection in IoT networks. 2018 17 ^th IEEE international conference on machine learning and applications (ICMLA), (pp. 1048-1053). IEEE. https://doi.org/10.1109/ICMLA.2018.00171.

20. Shafiq, M., Tian, Z., Bashir, A. K., Du, X., & Guizani, M. (2020). IoT malicious traffic identification using wrapper-based feature selection mechanisms. Computers & Security, 94, 101863. https://doi.org/10.1016/j.cose.2020.101863.

21. Baig, Z. A., Sanguanpong, S., Firdous, S. N., Nguyen, T. G., & So-In, C. (2020). Averaged dependence estimators for DoS attack detection in IoT networks. Future Generation Computer Systems, 102, 198-209. https://doi.org/10.1016/j.future.2019.08.007.

22. Bovenzi, G., Aceto, G., Ciuonzo, D., Persico, V., & Pescapé, A. (2020, December). A hierarchical hybrid intrusion detection approach in IoT scenarios. GLOBECOM 2020-2020 IEEE global communications conference, (pp. 1-7). IEEE. https://doi.org/10.1109/GLOBECOM42002.2020.9348167.

23. Shaukat, K., Luo, S., Chen, S., & Liu, D. (2020, October). Cyber threat detection using machine learning techniques: A performance evaluation perspective. 2020 international conference on cyber warfare and security (ICCWS), (pp. 1-6). IEEE. https://doi.org/10.1109/ICCWS48432.2020.9292388.

24. Soe, Y. N., Feng, Y., Santosa, P. I., Hartanto, R., & Sakurai, K. (2020). Towards a lightweight detection system for cyber attacks in the IoT environment using corresponding features. Electronics, 9(1), 144. https://doi.org/10.3390/electronics9010144.

25. Ullah, I., & Mahmoud, Q. H. (2020). A two-level flow-based anomalous activity detection system for IoT networks. Electronics, 9(3), 530. https://doi.org/10.3390/electronics9030530.

26. Shobana, M., & Poonkuzhali, S. (2020, December). A novel approach for detecting iot botnet using balanced network traffic attributes. International Conference on Service-Oriented Computing, (pp. 534-548). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-76352-7_48.

27. Syamsuddin, I., & Barukab, O. M. (2022). SUKRY: suricata IDS with enhanced kNN algorithm on raspberry Pi for classifying IoT botnet attacks. Electronics, 11(5), 737. https://doi.org/10.3390/electronics11050737.

28. Qian, G., Hu, L., Zhang, W., & He, W. (2023). A new intrusion detection model for industrial control system based on hierarchical interval-based BRB. Intelligent Systems with Applications, 18, 200239.

29. AlHaddad, U., Basuhail, A., Khemakhem, M., Eassa, F. E., & Jambi, K. (2023). Ensemble model based on hybrid deep learning for intrusion detection in smart grid networks. Sensors, 23(17), 7464. https://doi.org/10.3390/s23177464.

30. Karmous, N., Aoueileyine, M. O. E., Abdelkader, M., & Yous­sef, N. (2023, March). Enhanced Machine Learning-Based SDN Controller Framework for Securing IoT Networks. International Conference on Advanced Information Networking and Applications, (pp. 60-69). Cham: Springer International Publishing.

31. Koroniotis, N., Moustafa, N., Sitnikova, E., & Turnbull, B. (2019). Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset. Future Generation Computer Systems, 100, 779-796. https://doi.org/10.1016/j.future.2019.05.041.

32. Chiba, Z., Abghour, N., Moussaid, K., El omri, A., & Rida, M. (2019). Intelligent approach to build a deep neural network based IDS for cloud environment using combination of machine learning algorithms. Computers & Security, 86, 291-317.

33. Masdari, M., & Khezri, H. (2020). A survey and taxonomy of the fuzzy signature-based intrusion detection systems. Applied Soft Computing, 92, 106301.

34. Guezzaz, A., Benkirane, S., Azrour, M., & Khurram, S. (2021). A reliable network intrusion detection approach using decision tree with enhanced data quality. Security and Communication Networks, 2021(1), 1230593.

35. Batool, S., Abid, M. K., Salahuddin, M. A., Aziz, Y., Naeem, A., & Aslam, N. (2024). Integrating IoT and Machine Learning to Provide Intelligent Security in Smart Homes. Journal of Computing & Biomedical Informatics, 7(01), 224-238.

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