Deteksi Serangan Brute Force pada Jaringan IoT Menggunakan Algoritma Machine Learning Berbasis Dataset CICIoT2023
Abstract
Abstract The development of IoT, which is widely used by many people, continues to be used with various developments and new discoveries. As the process of development and time goes by, challenges also arise that can occur with various problems in IoT devices. One incident in the IoT problem is a brute force attack, which is an attempt to combine various credentials such as passwords that are carried out to force entry into the device until finding legitimate access by brute force or numerous attempts. From these activities collected into the CICIoT2023 dataset which is carried out to recognize these activities as an intrusion detection system (IDS) so that in the future it is hoped that it can monitor and prevent unwanted damage by performing data cleaning and applying machine learning. The results show that the accuracy reaches 0.9466 with the Random Forest model algorithm with the Random Undersampling (RUS) technique, thus demonstrating the effectiveness of the existing ensemble method in improving the stability of anomaly detection of brute force attacks in the IoT environment. Keywords: Internet of Things, Machine Learning, Cyber Security, CICIoT2023, Intrusion Detection System
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References
[1] P. Sun, S. Shen, Y. Wan, Z. Wu, Z. Fang, and X. Z. Gao, “A Survey of IoT Privacy Security: Architecture, Technology, Challenges, and Trends,” IEEE Internet Things J., vol. 11, no. 21, pp. 34567–34591, 2024, doi: 10.1109/JIOT.2024.3372518.
[2] 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, Jul. 2023, doi: 10.3390/s23135941.
[3] S. Sodagari, “Trends for Mobile IoT Crowdsourcing Privacy and Security in the Big Data Era,” IEEE Transactions on Technology and Society, vol. 3, no. 3, pp. 199–225, Jul. 2022, doi: 10.1109/TTS.2022.3191515.
[4] M. Bouzidi, N. Gupta, F. A. Cheikh, A. Shalaginov, and M. Derawi, “A Novel Architectural Framework on IoT Ecosystem, Security Aspects and Mechanisms: A Comprehensive Survey,” IEEE Access, vol. 10, pp. 101362–101384, 2022, doi: 10.1109/ACCESS.2022.3207472.
[5] M. Aziz Al Kabir, W. Elmedany, and M. S. Sharif, “Securing IoT Devices Against Emerging Security Threats: Challenges and Mitigation Techniques,” 2023, Taylor and Francis Ltd. doi: 10.1080/23742917.2023.2228053.
[6] W. A. H. Salman and C. H. Yong, “Overview of the CICIoT2023 Dataset for Internet of Things Intrusion Detection Systems,” Mesopotamian Journal of Big Data, vol. 2025, pp. 50–60, Jun. 2025, doi: 10.58496/MJBD/2025/004.
[7] A. El Hariri, M. Mouiti, O. Habibi, and M. Lazaar, “Improving Deep Learning Performance Using Sampling Techniques for IoT Imbalanced Data,” in Procedia Computer Science, Elsevier B.V., 2023, pp. 180–187. doi: 10.1016/j.procs.2023.09.026.
[8] A. Wakili and S. Bakkali, “A resilient IoT intrusion detection system using hybrid feature selection and explainable ensemble learning,” Results in Engineering, vol. 28, Dec. 2025, doi: 10.1016/j.rineng.2025.107392.
[9] E. Altulaihan, M. A. Almaiah, and A. Aljughaiman, “Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms,” Sensors, vol. 24, no. 2, p. 713, Jan. 2024, doi: 10.3390/s24020713.
[10] D. A. Devyatkin and O. G. Grigoriev, “Random Kernel Forests,” IEEE Access, vol. 10, pp. 77962– 77979, Nov. 2022, doi: 10.1109/ACCESS.2022.3193385.
[11] Z. Fan, S. Sohail, F. Sabrina, and X. Gu, “Sampling-Based Machine Learning Models for Intrusion Detection in Imbalanced Dataset,” Electronics 2024, Vol. 13, Page 1878, vol. 13, no. 10, p. 1878, May 2024, doi: 10.3390/ELECTRONICS13101878.
[12] I. S. Thaseen et al., “A Hadoop Based Framework Integrating Machine Learning Classifiers for Anomaly Detection in the Internet of Things,” Electronics 2021, Vol. 10, vol. 10, no. 16, Aug. 2021, doi: 10.3390/ELECTRONICS10161955.
[13] N. M. Mqadi, N. Naicker, and T. Adeliyi, “Solving Misclassification of the Credit Card Imbalance Problem Using Near Miss,” Math. Probl. Eng., vol. 2021, no. 1, p. 7194728, Jan. 2021, doi: 10.1155/2021/7194728.
[14] A. G. Putrada, M. Abdurohman, D. Perdana, and H. H. Nuha, “Shuffle Split-Edited Nearest Neighbor: A Novel Intelligent Control Model Compression for Smart Lighting in Edge Computing Environment,” Smart Innovation, Systems and Technologies, vol. 324, pp. 219–227, 2023, doi: 10.1007/978-981-19-7447-2_20.
[15] D.-H. Jeong et al., “A Comparative Study on the Influence of Undersampling and Oversampling Techniques for the Classification of Physical Activities Using an Imbalanced Accelerometer Dataset,” Healthcare 2022, Vol. 10, vol. 10, no. 7, Jul. 2022, doi: 10.3390/HEALTHCARE10071255.
