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Vol. 11, No. 4, November 2026 (Article in Progress)

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An Effective IoT Attack Detection System With A Symmetrical Uncertainty Factor and Random Forest

https://doi.org/10.22219/kinetik.v11i4.2778
Kurniabudi
Universitas Dinamika Bangsa
Chindra Saputra
Universitas Dinamika Bangsa
Janu Hadi Susilo
Universitas Dinamika Bangsa

Corresponding Author(s) : Kurniabudi

kurniabudi@unama.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 11, No. 4, November 2026 (Article in Progress)

Abstract

The rapid expansion of the Internet of Things (IoT) ecosystem has exposed critical security flaws, primarily because the high-dimensional, heavily imbalanced characteristics of IoT network traffic severely compromise existing intrusion detection systems (IDSs). A major limitation in current research is the decoupling of feature selection and classification, which leads to feature redundancy, high computational demands, and less-than-optimal detection rates. To overcome this limitation, we introduce an enhanced IoT attack detection architecture that seamlessly couples the Symmetrical Uncertainty (SU) filter for feature selection with the Random Forest (RF) classifier. Using the extensive CICIoT2023 dataset, which comprises benign traffic and 33 unique attack types, SU was applied to systematically filter out redundant attributes. Empirical evaluations reveal that the integrated SU-RF model, operating on a highly reduced subset of just 15 features (a 68% dimensionality reduction), attains exceptional full-set validation metrics: 99.997% accuracy, 99.996% precision, 99.995% recall, and a 99.995% F1-score. The model's generalization and robustness are further validated through 5-fold and 10-fold cross-validation, which consistently maintained accuracy exceeding 99.35%. When benchmarked against recent state-of-the-art approaches, such as the work by Gheni et al., the proposed framework achieves a 0.55% accuracy improvement while using 53% fewer features, significantly reducing computational costs. Ultimately, this study delivers a highly scalable, computationally lightweight, and robust IDS tailored specifically for the constraints of IoT environments.

Keywords

Intrusion Detection System Internet of Things Feature Selection Machine Learning Symmetrical Uncertainty Random Forest CICIoT2023
Kurniabudi, Chindra Saputra, & Janu Hadi Susilo. (2026). An Effective IoT Attack Detection System With A Symmetrical Uncertainty Factor and Random Forest. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 11(4). https://doi.org/10.22219/kinetik.v11i4.2778
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References
  1. S. S. Albouq, A. A. A. Sen, N. Almashf, M. Yamin, A. Alshanqiti, and N. M. Bahbouh, “A Survey of Interoperability Challenges and Solutions for Dealing With Them in IoT Environment,” IEEE Access, vol. 10, pp. 36416–36428, 2022, doi: 10.1109/ACCESS.2022.3162219.
  2. S. M. Ali, M. A. Rahu, S. Karim, G. M. Jatoi, and A. Sattar, “Internet of Things (IoT), Applications and Challenges: A Comprehensive Review,” Wirel. Pers. Commun., vol. 114, no. 2, pp. 1687–1762, 2020, doi: 10.1007/s11277-020-07446-4.
  3. A. Y. Khan, R. Latif, S. Latif, S. Tahir, G. Batool, and T. Saba, “Malicious Insider Attack Detection in IoTs Using Data Analytics,” IEEE Access, vol. 8, pp. 11743–11753, 2020, doi: 10.1109/ACCESS.2019.2959047.
  4. A. H. Farea and K. Küçük, “Detection of IoT Attacks via Machine Learning-Based Approaches with Cooja,” EAI Endorsed Trans. Internet Things, vol. 7, no. 28, pp. 1–12, 2022, doi: 10.4108/eetiot.v7i28.324.
  5. V. S. Magomadov, “The Industrial Internet of Things as one of the main drivers of Industry 4.0,” in IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, May 2020. doi: 10.1088/1757-899X/862/3/032101.
  6. J. C. S. Sicato, S. K. Singh, S. Rathore, and J. H. Park, “A comprehensive analysis of intrusion detection system for IoT environment,” J. Inf. Process. Syst., vol. 16, no. 4, pp. 975–990, Aug. 2020, doi: 10.3745/JIPS.03.0144.
  7. R. Adrian, A. J. Okke, M. A. R. Somardani, and T. Widiasari, “Determination of Attack Points on IoT Devices based on Particle Swarm Optimization to Support Intrusion Prevention System,” in 2022 5th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 2022, pp. 47–50. doi: 10.1109/ISRITI56927.2022.10053073.
  8. Y. Ma et al., “A Survey of DDoS Attack and Defense Technologies in Multiaccess Edge Computing,” IEEE Internet Things J., vol. 12, no. 2, pp. 1428–1452, 2025, doi: 10.1109/JIOT.2024.3490897.
  9. H. Fereidouni, O. Fadeitcheva, and M. Zalai, “IoT and Man‐in‐the‐Middle Attacks,” Secur. Priv., vol. 8, no. 2, 2025, doi: 10.1002/spy2.70016.
  10. F. S. Alrayes, M. Zakariah, S. U. Amin, Z. Iqbal Khan, and M. Helal, “Intrusion Detection in IoT Systems Using Denoising Autoencoder,” IEEE Access, vol. 12, no. August, pp. 122401–122425, 2024, doi: 10.1109/ACCESS.2024.3451726.
  11. A. Alqahtani and H. AlShaher, “Anomaly-Based Intrusion Detection Systems Using Machine Learning,” J. Cybersecurity & Inf. Manag., vol. 14, no. 1, 2024.
  12. U. Tahir, M. K. Abid, M. Fuzail, and N. Aslam, “Enhancing IoT Security through Machine Learning-Driven Anomaly Detection,” VFAST Trans. Softw. Eng., vol. 12, no. 2, pp. 1–13, 2024, doi: 10.21015/vtse.v12i2.1766.
  13. J. Zhang, “A Random Forest-Based Approach for Cybersecurity Attack Detection,” J. Next Comput., vol. 1, no. 1, pp. 1–12, 2025.
  14. H. A. Salman, A. Kalakech, and A. Steiti, “Random Forest Algorithm Overview,” Babylonian J. Mach. Learn., vol. 2024, pp. 69–79, 2024, doi: 10.58496/BJML/2024/007.
  15. C. Diallo, “IoT Anomaly Detection and Attack Identification Using Smart Traffic Classification Techniques,” in 2022 7th International Conference on Frontiers of Signal Processing (ICFSP), 2022, pp. 51–58. doi: 10.1109/ICFSP55781.2022.9924901.
  16. D. Rani and N. C. Kaushal, “Supervised Machine Learning Based Network Intrusion Detection System for Internet of Things,” 2020 11th Int. Conf. Comput. Commun. Netw. Technol. ICCCNT 2020, 2020, doi: 10.1109/ICCCNT49239.2020.9225340.
  17. Z. Chen, L. Zhou, and W. Yu, “ADASYN-Random forest-based intrusion detection model,” in Proceedings of the 2021 4th International Conference on Signal Processing and Machine Learning, 2021, pp. 152–159.
  18. P. Nimbalkar and D. Kshirsagar, “Feature selection for intrusion detection system in Internet-of-Things (IoT),” ICT Express, vol. 7, no. 2, pp. 177–181, Jun. 2021, doi: 10.1016/j.icte.2021.04.012.
  19. X. Liu and Y. Du, “Towards Effective Feature Selection for IoT Botnet Attack Detection Using a Genetic Algorithm,” Electronics, vol. 12, no. 5, 2023, doi: 10.3390/electronics12051260.
  20. A. K. Dey, G. P. Gupta, and S. P. Sahu, “Hybrid Meta-Heuristic based Feature Selection Mechanism for Cyber-Attack Detection in IoT-enabled Networks,” Procedia Comput. Sci., vol. 218, pp. 318–327, 2023, doi: 10.1016/j.procs.2023.01.014.
  21. H. Q. Gheni, W. K. Oleiwi, Z. Al-Barmani, and M. A. M. Alabdali, “Optimizing feature selection for intrusion detection: A hybrid approach using cuckoo search and particle swarm optimization,” Int. J. Saf. Secur. Eng., vol. 14, no. 6, pp. 1907–1912, 2024.
  22. M. Almohaimeed and F. Albalwy, “Enhancing IoT network security using feature selection for intrusion detection systems,” Appl. Sci., vol. 14, no. 24, p. 11966, 2024.
  23. R. Lalduhsaka and A. K. Khan, “Enhanced Intrusion Detection System Using a Two-Staged Feature Selection Method,” Secur. Priv., vol. 8, no. 3, p. e70025, 2025.
  24. S. Razdan, H. Gupta, and A. Seth, “Feature Selection Methods for Intrusion Detection Systems: A Performance Comparison,” in 2022 2nd Asian Conference on Innovation in Technology (ASIANCON), 2022, pp. 1–5. doi: 10.1109/ASIANCON55314.2022.9909253.
  25. S. Maldonado and J. López, “Dealing with high-dimensional class-imbalanced datasets: Embedded feature selection for SVM classification,” Appl. Soft Comput. J., vol. 67, pp. 94–105, 2018, doi: 10.1016/j.asoc.2018.02.051.
  26. D. Theng and K. K. Bhoyar, “Feature selection techniques for machine learning: a survey of more than two decades of research,” Knowl. Inf. Syst., vol. 66, no. 3, pp. 1575–1637, 2024.
  27. Z. Shi, Y. Liang, and B. Li, “Research on Attack Detection Based on Random Forest Improved Algorithm for Power Line Carrier Communication,” in 2022 7th International Conference on Computer and Communication Systems (ICCCS), 2022, pp. 784–788. doi: 10.1109/ICCCS55155.2022.9846063.
  28. M. Salem and S. Shahid, “Symmetrical uncertainty and random forest for the evaluation of gridded precipitation and temperature data,” Atmos. Res., vol. 230, no. July, p. 104632, 2019, doi: 10.1016/j.atmosres.2019.104632.
  29. A. F. A. H. Alnuaimi and T. H. K. Albaldawi, “An overview of machine learning classification techniques,” BIO Web Conf., vol. 97, pp. 1–24, 2024, doi: 10.1051/bioconf/20249700133.
  30. I. D. Mienye and Y. Sun, “A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects,” IEEE Access, vol. 10, no. September, pp. 99129–99149, 2022, doi: 10.1109/ACCESS.2022.3207287.
  31. S. Sathyanarayanan and B. R. Tantri, “Confusion matrix-based performance evaluation metrics,” African J. Biomed. Res., vol. 27, no. 4S, pp. 4023–4031, 2024.
  32. R. A. Ramadan and K. Yadav, “A novel hybrid intrusion detection system (IDS) for the detection of Internet of Things (IoT) network attacks,” Ann. Emerg. Technol. Comput., vol. 4, no. 5, pp. 61–74, 2020, doi: 10.33166/AETIC.2020.05.004.
  33. K. G. R. Narayan, S. Mookherji, V. Odelu, R. Prasath, A. C. Turlapaty, and A. K. Das, “IIDS: Design of Intelligent Intrusion Detection System for Internet-of-Things Applications,” in 2023 IEEE 7th Conference on Information and Communication Technology, CICT 2023, Aug. 2023. doi: 10.1109/CICT59886.2023.10455720.
  34. E. C. P. Neto, S. Dadkhah, R. Ferreira, A. Zohourian, R. Lu, and A. A. Ghorbani, “CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment,” Sensors, vol. 23, no. 13, 2023, doi: 10.3390/s23135941.
  35. W. A. H. Salman and C. H. Yong, “Overview of the CICIoT2023 Dataset for Internet of Things Intrusion Detection Systems,” Mesopotamian J. Big Data, vol. 2025, pp. 50–60, 2025, doi: 10.58496/MJBD/2025/004.
  36. M. Alharby, “Evaluating machine learning approaches for multiple attack classification with improved computational efficiency in IoT networks,” Sci. Rep., vol. 15, no. 1, pp. 1–18, 2025, doi: 10.1038/s41598-025-23711-7.
  37. J. Jabez, S. Gowri, S. Vigneshwari, J. A. Mayan, and S. Srinivasulu, “Anomaly Detection by Using CFS Subset and Neural Network with WEKA Tools,” Inf. Commun. Technol. Intell. Syst. Proc. ICTIS 2018, vol. 2, pp. 675–682, 2019.
  38. J. C. Mina, “Application of supervised learning algorithm to determine the quality of slippers in WEKA,” Int. J. Adv. Appl. Sci., vol. 10, no. 8, pp. 106–111, 2023, doi: 10.21833/ijaas.2023.08.012.
  39. M. B. Abdulkareem, “Analysis of the performance of algorithms (K-Means, Farthest First, Hierarchical) using the data analysis and modeling tool Weka,” Babylonian J. Mach. Learn., vol. 2025, pp. 13–26, 2025, doi: 10.58496/BJML/2025/002.
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