Intrusion Detection in IoT Environment Using Hyperparameters Tuned Machine and Deep Learning Models on the CICIoT2023 Dataset
Abstract
Internet of Things (IoT) technology has made our life connected, simple and smart by integrating physical objects to the internet in various fields. These are also systems capable of creating and transmitting data to users through various services. Since these objects with their limited resources are interconnected with each other via the internet, they are then vulnerables to many attacks. Given the constraints already mentioned, traditional intrusion detection systems (IDS) are inadequate and no longer sufficient. In this paper, we propose an intrusion detection taking on consideration the limited resources of IoT devices, using machine learning and deep learning combined with features engineering, data balancing method and hyperparameters tuning to achieve the best result. Using a wide range of evaluation metrics, including Accuracy, Precision, Recall, F1-score, confusion matrix and execution time, we have evaluated various machine and deep learning models including Support Vector Machine, Random Forest, VGGNet and Deep Neural Network, as well as an approach for features extraction such as features scaling and transformation. This study is carried out using a well-known, benchmark and real time dataset CICIoT2023, generated by IoT devices that includes thirty-three attacks, classified into seven categories, namely DDoS, DoS, Recon, Web-based, Brute Force, Spoofing, and Mirai.The experiment result demonstrates the effectiveness of Random Forest that accomplished with 91,89% in the accuracy and 92% in the precision, outperformed the others modeles in classifying attacks from normal traffic with a minimum time of execution.References
Madakam, S., Ramaswamy, R., & Tripathi, S. (2015). Internet of Things (IoT): A literature review. Journal of Computer and Communications, 3(5), 164-173.
De Michele, R., & Furini, M. (2019, September). Iot healthcare: Benefits, issues and challenges. In Proceedings of the 5th EAI international conference on smart objects and technologies for social good (pp. 160-164).
Zantalis, F., Koulouras, G., Karabetsos, S., & Kandris, D. (2019). A review of machine learning and IoT in smart transportation. Future Internet, 11(4), 94
Jeong, Y. S., & Park, J. H. (2019). IoT and smart city technology: challenges, opportunities, and solutions. Journal of Information Processing Systems, 15(2), 233- 238
Farooq, M. S., Riaz, S., Abid, A., Umer, T., & Zikria, Y. B. (2020). Role of IoT technology in agriculture: A systematic literature review. Electronics, 9(2), 319.
Moslehi, M. M. (2025). Exploring coverage and security challenges in wireless sensor networks: A survey. Computer Networks, 111096.
Abomhara, M., & Køien, G. M. (2015). Cyber security and the internet of things: vulnerabilities, threats, intruders and attacks. Journal of Cyber Security and Mobility, 65-88
Zarpel˜ao, B. B., Miani, R. S., Kawakani, C. T., & De Alvarenga, S. C. (2017). A survey of intrusion detection in Internet of Things. Journal of Network and Computer Applications, 84, 25-37.
Huang, H., Xiao, D., Wang, M., Liang, J., Li, M., Chen, L., & Liu, Y. (2025). Fog-driven communication-efficient and privacy-preserving federated learning based on compressed sensing. Computer Networks, 259, 111043.
Hussain, F., Hussain, R., Hassan, S. A., & Hossain, E. (2020). Machine learn- ing in IoT security: Current solutions and future challenges. IEEE Communications Surveys & Tutorials, 22(3), 1686-1721.
Amanullah, M. A., Habeeb, R. A. A., Nasaruddin, F. H., Gani, A., Ahmed, E., Nainar, A. S. M., ... & Imran, M. (2020). Deep learning and big data technologies for IoT security. Computer Communications, 151, 495-517
Nahida Islam1, Fahiba Farhin1 , Ishrat Sultana1 , M. Shamim Kaiser1 , Md. Sazzadur Rahman1 , Mufti Mahmud2 , A. S. M. Sanwar Hosen3 and Gi Hwan Cho. Towards Machine Learning Based Intrusion Detection in IoT Networks. Computers, Materials & Continua. DOI:10.32604/cmc.2021.018466
Lin, T. (2020, November). Deep learning for IoT. In 2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) (pp. 1-4).
Li, Y., Zuo, Y., Song, H., & Lv, Z. (2021). Deep learning in security of internet of things. IEEE Internet of Things Journal, 9(22), 22133-22146.
Abbass, W., Bakraouy, Z., Ba¨ına, A., & Bellafkih, M. (2018, October). Classifying IoT security risks using deep learning algorithms. In 2018 6th International Conference on Wireless Networks and Mobile Communications (WINCOM) (pp. 1-6). IEEE
Pokhrel, S., Abbas, R., & Aryal, B. (2021). IoT security: botnet detection in IoT using machine learning. arXiv preprint arXiv:2104.02231.
Ioannou, C., & Vassiliou, V. (2019, May). Classifying security attacks in IoT networks using supervised learning. In 2019 15th International conference on distributed computing in sensor systems (DCOSS) (pp. 652-658). IEEE.
Chirra, D. R. (2023). Deep Learning Techniques for Anomaly Detection in IoT Devices: Enhancing Security and Privacy. Revista de Inteligencia Artificial en Medicina, 14(1), 529-552
Berqia, A., Bouijij, H., Merimi, A., & Ouaggane, A. (2024, May) Detecting DDoS attacks using machine learning in IoT environment. In 2024 International Conference on Intelligent Systems and Computer Vision (ISCV) (pp. 1-8). doi: 10.1109/ISCV60512.2024.10620122.
El Yamani, Y., Fadili, Y., Kilani, J., El Kamoun, N., Baddi, Y., & Bensalah, F. (2024, July). Hybrid Models for IoT Security: Tackling Class Imbalance. In 2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM) (pp. 1-6). doi: 10.1109/WINCOM62286.2024.10656654.
Sarhan, M. Layeghy, S. Moustafa, N. Gallagher, M, Portmann, M. (2024). Feature extraction for ML based Intrusion Detection in IoT networks. Digital Communications and Networks. No 10 (2024): 205-216
Elouardi, S., Motii, A., Jouhari, M., Amadou, A. N. H., & Hedabou, M. (2024). A survey on Hybrid-CNN and LLMs for intrusion detection systems: Recent IoT datasets. IEEE Access
B. Ayyaz-ul-Haq Qureshi, H. Larijani, J. Ahmad and N. Mtetwa, “A heuristic intrusion detection system for internet-of-things (IoT),” in Intelligent Computing: Proc. of the 2019 Computing Conf., London, United Kingdom, pp. 86–98, 2019
K. Jiang, W. Wang, A. Wang and H. Wu, “Network intrusion detection combined hybrid sampling with deep hierarchical network,” IEEE Access, vol. 8, pp. 32464–32476, 2020
A. Dushimimana, T. Tao, R. Kindong and A. Nishyirimbere, “Bi-directional recurrent neural network for intrusion detection system (IDS) in the internet of things (IoT),” International Journal of Advanced Engineering Research and Science, vol. 7, no. 3, pp. 524–539, 2020.
Z. Li, P. Batta and L. Trajkovic, “Comparison of machine learning algorithms for detection of network intrusions,” in 2018 IEEE Int. Conf. on Systems, Man, and Cybernetics, Miyazaki, Japan, pp. 4248–4253, 2018
Neto, E. C. P., Dadkhah, S., Ferreira, R., Zohourian, A., Lu, R., & Ghorbani,(2023). CICIoT2023: A real-time dataset and benchmark for large-scale attacks in IoT environment. Sensors, 23(13), 5941.
Hiri, Mustafa; Chrayah, Mohamed; Ourdani, Nabil; Aknin, Noura. (2023). Machine Learning Techniques for Diabetes Classification: A Comparative Study. International Journal of Advanced Computer Science and Applications. 14. 10.14569/IJACSA.2023.0140982.
Abbas, A. M. A. Khan, S. Latif, M. Ajaz, A. A. Shah, (2022)”A new ensemble- based intrusion detection system for internet of things,” Arabian Journal for Science and Engineering, Springer
Zhou, S., Liang, W., Li, J., & Kim, J. U. (2018). Improved VGG model for road traffic sign recognition. Computers, Materials & Continua, 57(1), 11-24.
Manjula, P., & Priya, S. B. (2022). An effective network intrusion detection and classification system for securing WSN using VGG-19 and hybrid deep neural network techniques. Journal of Intelligent & Fuzzy Systems, 43(5), 6419-6432
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