A Comparison of Enhanced Ensemble Learning Techniques for Internet of Things Network Attack Detection
Keywords:
Attack Detection, Ensemble Learning, Internet of Things Network
Abstract
Over the past few decades, the Internet of Things (IoT) has become increasingly significant due to its capacity to enable low-cost device and sensor communication. Implementation has opened up many new opportunities in terms of efficiency, productivity, convenience, and security. However, it has also brought about new privacy and data security challenges, interoperability, and network reliability. The research issue is that IoT devices are frequently open to attacks. Certain machine learning (ML) algorithms still struggle to handle imbalanced data and have weak generalization skills when compared to ensemble learning. The research aims to develop security for IoT networks based on enhanced ensemble learning by using Grid Search and Random Search techniques. The method used is the ensemble learning approach, which consists of Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). This study uses the UNSW-NB15 IoT dataset. The study's findings demonstrate that XGBoost performs better than other methods at identifying IoT network attacks. By employing Grid Search and Random Search optimization, XGBoost achieves an accuracy rate of 98.56% in binary model measurements and 97.47% on multi-class data. The findings underscore the efficacy of XGBoost in bolstering security within IoT networks.
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References
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[29] A. M. Mahfouz, A. Abuhussein, F. S. Alsubaei, and S. G. Shiva, “Toward A Holistic, Efficient, Stacking Ensemble Intrusion Detection System using a Real Cloud-based Dataset,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 9, pp. 950–962, 2022, doi: 10.14569/IJACSA.2022.01309110.
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[31] Y. Alotaibi and M. Ilyas, “Ensemble-Learning Framework for Intrusion Detection to Enhance Internet of Things’ Devices Security,” Sensors, vol. 23, no. 12, 2023, doi: 10.3390/s23125568.
[32] O. O. Olasehinde, “A Stacked Ensemble Intrusion Detection Approach for the Protection of Information System,” International Journal for Information Security Research, vol. 10, no. 1, pp. 910–923, 2020, doi: 10.20533/ijisr.2042.4639.2020.0105.
[33] D. Srivastav and P. Srivastava, “A two-tier hybrid ensemble learning pipeline for intrusion detection systems in IoT networks,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 4, pp. 3913–3927, 2023, doi: 10.1007/s12652-022-04461-0.
[34] D. N. Mhawi and S. H. Hashim, “Proposed Hybrid Ensemble Learning Algorithms for an Efficient Intrusion Detection System,” Iraqi Journal of Computer, Communication, Control and System Engineering, vol. 22, no. 2, pp. 73–84, 2022, doi: 10.33103/uot.ijccce.22.2.7.
[35] C. Iwendi, S. Khan, J. H. Anajemba, M. Mittal, M. Alenezi, and M. Alazab, “The use of ensemble models for multiple class and binary class classification for improving intrusion detection systems,” Sensors (Switzerland), vol. 20, no. 9, pp. 1–37, 2020, doi: 10.3390/s20092559.
[36] R. Golchha, A. Joshi, and G. P. Gupta, “Voting-based Ensemble Learning approach for Cyber Attacks Detection in Industrial Internet of Things,” Procedia Computer Science, vol. 218, pp. 1752–1759, 2022, doi: 10.1016/j.procs.2023.01.153.
[37] R. Gangula, M. M. Vutukuru, and M. Ranjeeth Kumar, “Intrusion Attack Detection Using Firefly Optimization Algorithm and Ensemble Classification Model,” Wireless Personal Communications, vol. 132, no. 3, pp. 1899–1916, 2023, doi: 10.1007/s11277-023-10687-8.
[38] Q. Abbas, S. Hina, H. Sajjad, K. S. Zaidi, and R. Akbar, “Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems,” PeerJ Computer Science, vol. 9, 2023, doi: 10.7717/peerj-cs.1552.
[39] M. H. L. Louk and B. A. Tama, “Dual-IDS: A bagging-based gradient boosting decision tree model for network anomaly intrusion detection system,” Expert Systems with Applications, vol. 213, no. PB, p. 119030, 2023, doi: 10.1016/j.eswa.2022.119030.
[40] M. M. Otoom, K. N. A. Sattar, and M. Al Sadig, “Ensemble Model for Network Intrusion Detection System Based on Bagging Using J48,” Advances in Science and Technology Research Journal, vol. 17, no. 2, pp. 322–329, 2023, doi: 10.12913/22998624/161820.
[41] D. Mishra, B. Naik, J. Nayak, A. Souri, P. B. Dash, and S. Vimal, “Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network,” Digital Communications and Networks, vol. 9, no. 1, pp. 125–137, 2023, doi: 10.1016/j.dcan.2022.10.004.
[42] Q. A. Al-Haija and M. Al-Dala’ien, “ELBA-IoT: An Ensemble Learning Model for Botnet Attack Detection in IoT Networks,” Journal of Sensor and Actuator Networks, vol. 11, no. 1, 2022, doi: 10.3390/jsan11010018.
[43] H. C. Lin, P. Wang, K. M. Chao, W. H. Lin, and Z. Y. Yang, “Ensemble learning for threat classification in network intrusion detection on a security monitoring system for renewable energy,” Applied Sciences (Switzerland), vol. 11, no. 23. 2021, doi: 10.3390/app112311283.
[44] A. G. Sooai, S. D. B. Mau, Y. C. H. Siki, D. J. Manehat, S. C. Sianturi, and A. H. Mondolang, “Optimizing Lantana Classification: High-Accuracy Model Utilizing Feature Extraction,” Jurnal Ilmiah Kursor, vol. 12, no. 2, pp. 49–58, 2023, doi: 10.21107/kursor.v12i2.347.
[45] H. Alibrahim and S. A. Ludwig, “Hyperparameter Optimization: Comparing Genetic Algorithm against Grid Search and Bayesian Optimization,” IEEE Congress on Evolutionary Computation, CEC 2023, pp. 1–9, 2021.
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Published
2024-06-18
How to Cite
Ismanto, E., Al Amien, J., & Vitriani, V. (2024). A Comparison of Enhanced Ensemble Learning Techniques for Internet of Things Network Attack Detection. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 23(3), 541-554. https://doi.org/https://doi.org/10.30812/matrik.v23i3.3885
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Articles
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