JCEEES
Journal of Computer & Electrical and Electronics Engineering Sciences

JCEEES aims to publish original articles covering the theoretical foundations of major computer, electronic and electrical engineering sciences, as well as academic, commercial and educational aspects that propose new ideas for the application and design of artificial intelligence, software and information systems. In addition to wide-ranging regular topics, JCEEES also makes it a principle to include special topics covering specific topics in all areas of interest mainly in computational medicine, artificial intelligence, computer science, and electrical & electronic engineering science.

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Original Article
Explainable AI-driven IoT botnet detection for 6G networks
Abstract
The rapid proliferation of Internet of Things (IoT) devices in smart homes, healthcare, and industrial automation introduces severe security challenges, especially with the transition toward 6G intelligent networks. Traditional machine learning models achieve high detection accuracy for IoT botnets but often lack interpretability, limiting their trustworthiness in mission-critical 6G environments. This study proposes an explainable AI-driven IoT botnet detection framework utilizing the N-BaIoT dataset to bridge this gap. The framework integrates correlation-based feature selection, mutual information analysis, ANOVA filtering, and low-variance elimination to construct a lightweight yet discriminative feature space. Several ensemble learning algorithms were evaluated, with LightGBM achieving the highest accuracy (99.82%) and exceptional stability across cross-validation folds. To enhance transparency, model decisions were explained using SHAP and LIME, revealing key traffic features that differentiate benign and malicious flows. The proposed system demonstrates not only high detection precision but also human-interpretable reasoning, fulfilling essential trust and reliability requirements for next-generation 6G IoT security architectures.

Keywords : IoT botnet detection, explainable Artificial Intelligence, 6G networks, cybersecurity, feature selection, anomaly detection

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1. Abbasi, F., Naderan, M., & Alavi, S. E. (2021, May). Anomaly detection in internet of things using feature selection and classification based on logistic regression and artificial neural network on N-BaIoT dataset. In 2021 5th International Conference on Internet of Things and Applications (IoT) (pp. 1-7). IEEE. https://doi.org/10.1109/IoT52625.2021.9469605
2. Abbasi, F., Naderan, M., & Alavi, S. E. (2025). Dimensionality reduction with deep learning classification for botnet detection in the internet of things. Expert Systems with Applications, 267, 126149. https://doi.org/10.1016/j.eswa.2024.126149
3. Alani, M. M., & Damiani, E. (2023). XRecon: an explainbale IoT reconnaissance attack detection system based on ensemble learning. Sensors, 23(11), 5298. https://doi.org/10.3390/s23115298
4. Ali, S., Ghazal, R., Qadeer, N., Saidani, O., Alhayan, F., Masood, A., ..., & Gupta, D. (2024). A novel approach of botnet detection using hybrid deep learning for enhancing security in IoT networks. Alexandria Engineering Journal, 103, 88-97. https://doi.org/10.1016/j.aej.2024.05.113
5. Alkahtani, H., & Aldhyani, T. H. H. (2021). Botnet attack detection by using CNN-LSTM model for internet of things applications. Security and Communication Networks, 2021, 3806459. https://doi.org/10.1155/2021/3806459
6. Alotaibi, A., & Barnawi, A. (2023). Securing massive IoT in 6G: Recent solutions, architectures, future directions. Internet of Things, 22, 100715. https://doi.org/10.1016/j.iot.2023.100715
7. Al-Othman, Z., Alkasassbeh, M., & Al-Haj Baddar, S. W. (2020). An efficient approach to detect IoT botnet attacks using machine learning. Journal of High Speed Networks, 26(3), 241-254. https://doi.org/10.3233/JHS-200641
8. Alsamiri, J., & Alsubhi, K. (2019). Internet of things cyber attacks detection using machine learning. International Journal of Advanced Computer Science and Applications, 10(12), 627-634. https://doi.org/10.14569/IJACSA.2019.0101280
9. Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324
10. 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). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939785
11. Do, P. H., Le, D. T., Vishnevsky, V. M., Berezkin, A., & Kirichek, R. (2024). A horizontal federated learning approach to IoT malware traffic detection: an empirical evaluation with N-BaIoT dataset. In 2024 26th International Conference on Advanced Communications Technology (ICACT) (pp. 1494-1506). IEEE. https://doi.org/10.23919/ICACT60172.2024.10471929
12. Emmanuel, E. J. (2025). Systematic review of 6G-IoT privacy risks, emerging threats, mitigation strategies, and cybersecurity. Asian Journal of Advanced Research and Reports, 19(9), 180-190. https://doi.org/10.9734/ajarr/2025/v19i91151
13. Garcia, S., Parmisano, A., & Erquiaga, M. J. (2020). IoT-23: a labeled dataset with malicious and benign IoT network traffic (Version 1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4743746
14. Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63, 3-42. https://doi.org/10.1007/s10994-006-6226-1
15. Guerra-Manzanares, A., Medina-Galindo, J., Bahsi, H., & Nõmm, S. (2022). Using MedBIoT dataset to build effective machine learning-based IoT botnet detection systems. In S. Furnell, P. Mori, E. Weippl, & O. Camp (Eds.), Information systems security and privacy: 6th International Conference, ICISSP 2020, revised selected papers (pp. 222-243). Springer. https://doi.org/10.1007/978-3-030-94900-6_11
16. Gummadi, A. N., Napier, J. C., & Abdallah, M. (2024). XAI-IoT: an explainable AI framework for enhancing anomaly detection in IoT systems. IEEE Access, 12, 71024-71054. https://doi.org/10.1109/ACCESS.2024.3402446
17. Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157-1182.
18. Karanja, E. M., Masupe, S., & Jeffrey, M. G. (2020). Analysis of internet of things malware using image texture features and machine learning techniques. Internet of Things, 9, 100153. https://doi.org/10.1016/j.iot.2019.100153
19. Kaur, N., & Gupta, L. (2025). Securing the 6G-IoT environment: a framework for enhancing transparency in Artificial Intelligence decision-making through explainable Artificial Intelligence. Sensors, 25(3), 854. https://doi.org/10.3390/s25030854
20. Khan, W., Usama, M., Khan, M. S., Saidani, O., Al Hamadi, H., Alnazzawi, N., ..., & Ahmad, J. (2025). Enhancing security in 6G-enabled wireless sensor networks for smart cities: a multi-deep learning intrusion detection approach. Frontiers in Sustainable Cities, 7, 1580006. https://doi.org/10.3389/frsc.2025.1580006
21. 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
22. Krishna, E. S. P., & Thangavelu, A. (2021). Attack detection in IoT devices using hybrid metaheuristic lion optimization algorithm and firefly optimization algorithm. International Journal of System Assurance Engineering and Management, 1-14. https://doi.org/10.1007/s13198-021-01150-7
23. Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. Springer. https://doi.org/10.1007/978-1-4614-6849-3
24. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765-4774.
25. Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Shabtai, A., ..., & Elovici, Y. (2018). N-BaIoT-Network-based detection of IoT botnet attacks using deep autoencoders. IEEE Pervasive Computing, 17(3), 12-22. https://doi.org/10.1109/MPRV.2018.03367731
26. Montgomery, D. C. (2017). Design and analysis of experiments (9th ed.). John Wiley & Sons.
27. Nazir, A., He, J., Zhu, N., Qureshi, S. S., Qureshi, S. U., Ullah, F., ..., & Pathan, M. S. (2024). A deep learning-based novel hybrid CNN-LSTM architecture for efficient detection of threats in the IoT ecosystem. Ain Shams Engineering Journal, 15(7), 102777. https://doi.org/10.1016/j.asej.2024.102777
28. Rallapalli, S., Tiwari, V. K., Sagar, S. D. V., & Pareek, P. K. (2023). Detecting the anomaly attacks in industrial IoT using hybrid feature selection based modified convolutional neural network. In 2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT) (pp. 1-7). IEEE. https://doi.org/10.1109/EASCT59475.2023.10393443
29. Rasheed, I., & Mostafa, H. (2025). Federated learning-based anomaly detection for zero-day attack prevention in 6G network slices. Computer Networks, 268, 111376. https://doi.org/10.1016/j.comnet.2025.111376
30. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?”: explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135-1144). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939778
31. Saied, M., & Guirguis, S. (2025). Explainable Artificial Intelligence for botnet detection in internet of things. Scientific Reports, 15, 7632. https://doi.org/10.1038/s41598-025-90420-6
32. Saied, M., Guirguis, S., & Madbouly, M. (2023). A comparative analysis of using ensemble trees for botnet detection and classification in IoT. Scientific Reports, 13, 21632. https://doi.org/10.1038/s41598-023-48681-6
33. Santhadevi, D., & Janet, B. (2023). Stacked deep learning framework for edge-based intelligent threat detection in IoT network. The Journal of Supercomputing, 79(11), 12622-12655. https://doi.org/10.1007/s11227-023-05153-y
34. Savant, R. V., M, S., Mohan, P. K., Bhaskaran, S., & Rajagopal, S. M. (2024). A comparative analysis of black-box and white-box models for IoT botnet detection. In 2024 IEEE 9th International Conference for Convergence in Technology (I2CT). IEEE. https://doi.org/10.1109/I2CT61223.2024.10544270
35. Serhane, A., Ibrahimi, K., Hamzaoui, E.-M., Jouhari, M., & Ben-Othman, J. (2025). IoT intrusion detection using machine learning classifiers and PCA dimensionality reduction for N-BaIoT dataset. In ICC 2025-IEEE International Conference on Communications (pp. 5809-5814). IEEE. https://doi.org/10.1109/ICC52391.2025.11161305
36. Son, B. D., Hoa, N. T., Chien, T. V., Khalid, W., Ferrag, M. A., ..., & Debbah, M. (2024). Adversarial attacks and defenses in 6G network-assisted IoT systems. IEEE Internet of Things Journal, 11(11), 19168-19187. https://doi.org/10.1109/JIOT.2024.3373808
37. Steinbach, M., & Tan, P.-N. (2009). kNN: K-nearest neighbors. In X. Wu & V. Kumar (Eds.), The top ten algorithms in data mining (pp. 151-161). Chapman & Hall/CRC. https://doi.org/10.1201/9781420089653-15
38. Trajanovski, T., & Zhang, N. (2021). An automated and comprehensive framework for IoT botnet detection and analysis (IoT-BDA). IEEE Access, 9, 124360-124383. https://doi.org/10.1109/ACCESS.2021.3110188
39. Ullah, S., Wu, J., Lin, Z., Kamal, M. M., Mostafa, H., ..., & Chuah, T. C. (2025). Comparative analysis of deep learning and traditional methods for IoT botnet detection using a multi-model framework across diverse datasets. Scientific Reports, 15, 31072. https://doi.org/10.1038/s41598-025-16553-w
40. Wang, D., Zhang, Y., & Zhao, Y. (2017). LightGBM: an effective miRNA classification method in breast cancer patients. In Proceedings of the 2017 International Conference on Computational Biology and Bioinformatics (pp. 7-11). Association for Computing Machinery. https://doi.org/10.1145/3155077.3155079
Article available in :
Volume 4, Issue 2, 2026
Page : 46-55
https://doi.org/10.51271/JCEEES-0036
Article Information
Received : 07 Kas 2025
Accepted : 30 Ara 2025
Published : 13 Eki 2026
Corresponding Author :

Merve Güllü: Department of R&D, Türk Telekom, Ankara, Turkiye

[email protected]
Citation : Güllü, M., Atagün, E., Biroğul, S., & Barışçı, N. (2026). Explainable AI-driven IoT botnet detection for 6G networks. J Comp Electr Electron Eng Sci, 4(2), 46-55. https://doi.org/10.51271/JCEEES-0036

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