Optimasi Klasifikasi Multikelas Jenis Serangan Jaringan IOT
DOI:
https://doi.org/10.30998/jrami.v7i03.1342Keywords:
ToN_IoT, klasifikasi multikelas, SMOTENC, XGBoost, Internet of ThingsAbstract
The development of the Internet of Things has encouraged the use of connected devices across various sectors, including healthcare, energy, transportation, and household environments. However, the heterogeneous and dynamic nature of IoT networks, along with their reliance on wireless communication, makes them vulnerable to various cyber threats. This study aims to develop and evaluate an intrusion detection model for multiclass classification in IoT networks using the ToN_IoT dataset. The models used in this study are Random Forest and XGBoost, tested under several scenarios, including baseline models, models with hyperparameter tuning, and the application of SMOTENC to XGBoost to address class imbalance. The research stages include exploratory data analysis, data cleaning, feature selection, data splitting, feature encoding, data balancing, modeling, and evaluation using accuracy, precision, recall, F1-score, training time, and confusion matrix. The results show that all models achieved high performance, with accuracy and F1-score values above 99%. The best performance was obtained by XGBoost with SMOTENC, achieving an accuracy of 99.59% and an F1-score of 99.59%.
Downloads
References
Choudhary, A. (2024). Internet of Things: a comprehensive overview, architectures, applications, simulation tools, challenges and future directions. Discover Internet of Things, 4(1), 31.
Čolaković, A., & Hadžialić, M. (2018). Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues. Computer Networks, 144, 17–39.
Chanal, P. M., & Kakkasageri, M. S. (2020). Security and Privacy in IoT: A Survey. Wireless Personal Communications, 115(2), 1667–1693.
Li, S., Xu, L. Da, & Zhao, S. (2015). The internet of things: a survey. Information Systems Frontiers, 17(2), 243–259.
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Communications Surveys & Tutorials, 17(4), 2347–2376.
Laghari, A. A., Li, H., Khan, A. A., Shoulin, Y., Karim, S., & Khani, M. A. K. (2024). Internet of Things (IoT) applications security trends and challenges. Discover Internet of Things, 4(1), 36.
Greengard, S. (2021). The Internet of Things, revised and updated edition. MIT Press.
Hathaliya, J. J., & Tanwar, S. (2020). An exhaustive survey on security and privacy issues in Healthcare 4.0. Computer Communications, 153, 311–335.
Jardine, E. (2018). Mind the denominator: towards a more effective measurement system for cybersecurity. Journal of Cyber Policy, 3(1), 116–139.
Gugueoth, V., Safavat, S., & Shetty, S. (2023). Security of Internet of Things (IoT) using federated learning and deep learning — Recent advancements, issues and prospects. ICT Express, 9(5),
Deep, S., Zheng, X., Jolfaei, A., Yu, D., Ostovari, P., & Kashif Bashir, A. (2022). A survey of security and privacy issues in the Internet of Things from the layered context. Transactions on Emerging Telecommunications Technologies, 33(6).
Soni, S., Remli, M. A., Daud, K. M., & Al Amien, J. (2024). Performance evaluation of multiclass classification models for ToN-IoT network device datasets. Indonesian Journal of Electrical Engineering and Computer Science, 35(1), 485.
Gad, A. R., Haggag, M., Nashat, A. A., & Barakat, T. M. (2022). A Distributed Intrusion Detection System using Machine Learning for IoT based on ToN-IoT Dataset. International Journal of Advanced Computer Science and Applications, 13(6).
Félix, B. A. H., Victoire, K. E., Placide, N. N. C., Pacôme, B., & Pascal, A. O. (2025). Intrusion Detection for Edge-IoT Using LSTM-Autoencoder. Open Journal of Applied Sciences, 15(09), 2638–2647.
Dharini, N., Janani, V. S., & Katiravan, J. (2026). Efficient detection of intrusions in TON-IoT dataset using hybrid feature selection approach. Scientific Reports, 16(1), 7763.
Moustafa, N. (2021). A new distributed architecture for evaluating AI-based security systems at the edge: Network TON_IoT datasets. Sustainable Cities and Society, 72, 102994.
Alsaedi, A., Moustafa, N., Tari, Z., Mahmood, A., & Anwar, A. (2020). TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems. IEEE Access, 8, 165130–165150.
Booij, T. M., Chiscop, I., Meeuwissen, E., Moustafa, N., & Hartog, F. T. H. den. (2022). ToN_IoT: The Role of Heterogeneity and the Need for Standardization of Features and Attack Types in IoT Network Intrusion Data Sets. IEEE Internet of Things Journal, 9(1), 485–496.
Latif, S., Huma, Z. e, Jamal, S. S., Ahmed, F., Ahmad, J., Zahid, A., Dashtipour, K., Aftab, M. U., Ahmad, M., & Abbasi, Q. H. (2022). Intrusion Detection Framework for the Internet of Things Using a Dense Random Neural Network. IEEE Transactions on Industrial Informatics, 18(9), 6435–6444.
Simangunsong, J., Simanjuntak, M. S., & Simanjuntak, N. D. (2024). Mental disorder classification with exploratory data analysis (EDA). Journal of Intelligent Decision Support System (IDSS), 7(3), 210–217.
Van den Broeck, J., Argeseanu Cunningham, S., Eeckels, R., & Herbst, K. (2005). Data Cleaning: Detecting, Diagnosing, and Editing Data Abnormalities. PLoS Medicine, 2(10), e267.
Dhanvijay, D. M., Dhanvijay, M. M., & Kamble, V. H. (2025). Cyber intrusion detection using ensemble of deep learning with prediction scoring based optimized feature sets for IOT networks. Cyber Security and Applications, 3, 100088.
Pargent, F., Pfisterer, F., Thomas, J., & Bischl, B. (2022). Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features. Computational Statistics, 37(5), 2671–2692.
M, A., Savarimuthu, N., & Bhanu, S. M. S. (2025). WoEEE: a hybrid approach for enhancement of categorical data transformation. International Journal of Data Science and Analytics, 20(7), 6635–6663.
Richman, R., & Wuthrich, M. V. (2023). High-Cardinality Categorical Covariates in Network Regressions. SSRN Electronic Journal.
Bouke, M. A., & Abdullah, A. (2023). An empirical study of pattern leakage impact during data preprocessing on machine learning-based intrusion detection models reliability. Expert Systems with Applications, 230, 120715.
Joseph, V. R. (2022). Optimal ratio for data splitting. Statistical Analysis and Data Mining: The ASA Data Science Journal, 15(4), 531–538.
Sivakumar, M., Parthasarathy, S., & Padmapriya, T. (2024). Trade-off between training and testing ratio in machine learning for medical image processing. PeerJ Computer Science, 10, e2245.
Anitha, M., Savarimuthu, N., & Bhanu, S. M. S. (2025). Chi-Square Target Encoding for Categorical Data Representation: A Real-World Sensor Data Case Study. SN Computer Science, 6(3), 228.
Mujahid, M., Kına, E., Rustam, F., Villar, M. G., Alvarado, E. S., De La Torre Diez, I., & Ashraf, I. (2024). Data oversampling and imbalanced datasets: an investigation of performance for machine learning and feature engineering. Journal of Big Data, 11(1), 87.
Fonseca, J., & Bacao, F. (2023). Geometric SMOTE for imbalanced datasets with nominal and continuous features. Expert Systems with Applications, 234, 121053.
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32.
Chen, T., & Guestrin, C. (2016). XGBoost. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.
Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. The Journal of Machine Learning Research, 13, 281–305.
Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427–437.
Mortaz, E. (2020). Imbalance accuracy metric for model selection in multi-class imbalance classification problems. Knowledge-Based Systems, 210, 106490.
Kasina, D., Reddy, V. S., Rajamanickam, S., Jose, J., & Palani, B. (2026). A novel intrusion detection system for class imbalance datasets using hybrid sampling with deep learning techniques. Information Sciences, 741, 123208.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Sunu Ilham Pradika, Hidayat Ramadhani, Imelda Imelda (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
