1
Higher Colleges of Technology, United Arab Emirates
(ratassi@hct.ac.ae)
Abstract :
The proliferation of Internet of Things (IoT) devices has ushered in an era of unprecedented connectivity and innovation. However, this interconnected landscape also presents unique security challenges, necessitating robust intrusion detection mechanisms. In this research, we present a comprehensive study of anomaly detection in IoT networks, leveraging advanced machine learning techniques. Specifically, we employ the Gated Recurrent Unit (GRU) architecture as the backbone network to capture temporal dependencies within IoT traffic. Furthermore, our approach embraces hierarchical federated training to ensure scalability and privacy preservation across distributed IoT devices. Our experimental design encompasses public IoT datasets, facilitating rigorous evaluation of the model's performance and adaptability. Results indicate that our GRU-based model excels in identifying a spectrum of attacks, from Distributed Denial of Service (DDoS) incursions to SQL injection attempts. Visualizations of learning curves, Receiver Operating Characteristic (ROC) curves, and confusion matrices offer insights into the model's learning process, discriminatory power, and classification performance. Our findings contribute to the evolving landscape of IoT security, offering a roadmap for enhancing the resilience of interconnected systems in an era of increasing connectivity.
Keywords :
Internet of Things (IoT); Anomaly Detection Algorithms; Intrusion Detection Systems; Machine Learning; Network Anomalies; Cybersecurity in IoT
References :
[1] Xu, H., Sun, Z., Cao, Y., & Bilal, H. (2023). A data-driven approach for intrusion and anomaly detection using automated machine learning for the Internet of Things. Soft Computing, 1-13.
[2] Azumah, S. W., Elsayed, N., Adewopo, V., Zaghloul, Z. S., & Li, C. (2021, June). A deep lstm based approach for intrusion detection iot devices network in smart home. In 2021 IEEE 7th World Forum on Internet of Things (WF-IoT) (pp. 836-841). IEEE.
[3] Maniriho, P., Niyigaba, E., Bizimana, Z., Twiringiyimana, V., Mahoro, L. J., & Ahmad, T. (2020, November). Anomaly-based intrusion detection approach for IoT networks using machine learning. In 2020 international conference on computer engineering, network, and intelligent multimedia (CENIM) (pp. 303-308). IEEE.
[4] Emeç, M., & Özcanhan, M. H. (2022). A hybrid deep learning approach for intrusion detection in IoT networks. Advances in Electrical and Computer Engineering, 22(1), 3-12.
[5] Akter, M., Dip, G. D., Mira, M. S., Abdul Hamid, M., & Mridha, M. F. (2020). Construing attacks of internet of things (IoT) and a prehensile intrusion detection system for anomaly detection using deep learning approach. In International Conference on Innovative Computing and Communications: Proceedings of ICICC 2019, Volume 2 (pp. 427-438). Springer Singapore.
[6] Kale, R., Lu, Z., Fok, K. W., & Thing, V. L. (2022, May). A hybrid deep learning anomaly detection framework for intrusion detection. In 2022 IEEE 8th Intl Conference on Big Data Security on Cloud(BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing,(HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS) (pp. 137-142). IEEE.
[7] A. Abdel-Monem and M. . Abouhawwash, “A Machine Learning Solution for Securing the Internet of Things Infrastructures”, SMIJ, vol. 1, Oct. 2022. https://doi.org/10.61185/SMIJ.HPAO9103
[8] Selvapandian, D., & Santhosh, R. (2021). Deep learning approach for intrusion detection in IoT-multi cloud environment. Automated Software Engineering, 28, 1-17.
[9] Mohammed I. Alghamdi, A Comprehensive Analysis of Cyber Security Protection Approaches for Financial Firms: A Case of Al Rajhi Bank, Saudi Arabia, Journal of Cybersecurity and Information Management, Vol. 9 , No. 1 , (2022) : 8-17 (Doi : https://doi.org/10.54216/JCIM.090101)
[10] Vikram, A. (2020, June). Anomaly detection in network traffic using unsupervised machine learning approach. In 2020 5th International Conference on Communication and Electronics Systems (ICCES) (pp. 476-479). IEEE.
[11]A. M.Ali and A. Abdelhafeez, “DeepHAR-Net: A Novel Machine Intelligence Approach for Human Activity Recognition from Inertial Sensors”, SMIJ, vol. 1, Nov. 2022. https://doi.org/10.61185/SMIJ.2022.8463
[12] Vaiyapuri, T., Sbai, Z., Alaskar, H., & Alaseem, N. A. (2021). Deep learning approaches for intrusion detection in IIoT networks–opportunities and future directions. International Journal of Advanced Computer Science and Applications, 12(4).
[13] Roy, B., & Cheung, H. (2018, November). A deep learning approach for intrusion detection in internet of things using bi-directional long short-term memory recurrent neural network. In 2018 28th international telecommunication networks and applications conference (ITNAC) (pp. 1-6). IEEE.
[14] Hasan, M., Islam, M. M., Zarif, M. I. I., & Hashem, M. M. A. (2019). Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet of Things, 7, 100059.
[15] Tyagi, H., & Kumar, R. (2021). Attack and Anomaly Detection in IoT Networks Using Supervised Machine Learning Approaches. Revue d'Intelligence Artificielle, 35(1).
[16] Sadikin, F., & Kumar, S. (2020, May). ZigBee IoT Intrusion Detection System: A Hybrid Approach with Rule-based and Machine Learning Anomaly Detection. In IoTBDS (pp. 57-68).
[17] Tabassum, A., Erbad, A., & Guizani, M. (2019, June). A survey on recent approaches in intrusion detection system in IoTs. In 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC) (pp. 1190-1197). IEEE.
[18] Sharma, B., Sharma, L., & Lal, C. (2019, December). Anomaly detection techniques using deep learning in IoT: a survey. In 2019 International conference on computational intelligence and knowledge economy (ICCIKE) (pp. 146-149). IEEE.
[19] Bovenzi, G., Aceto, G., Ciuonzo, D., Persico, V., & Pescapé, A. (2020, December). A hierarchical hybrid intrusion detection approach in IoT scenarios. In GLOBECOM 2020-2020 IEEE global communications conference (pp. 1-7). IEEE.
[20] Dawoud, A., Sianaki, O. A., Shahristani, S., & Raun, C. (2020, December). Internet of things intrusion detection: A deep learning approach. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1516-1522). IEEE.
| Style | # |
|---|---|
| MLA | Reem Atassi. "Anomaly Detection in IoT Networks: Machine Learning Approaches for Intrusion Detection." Fusion: Practice and Applications, Vol. 13, No. 1, 2023 ,PP. 126-134 (Doi : https://doi.org/10.54216/FPA.130110) |
| APA | Reem Atassi. (2023). Anomaly Detection in IoT Networks: Machine Learning Approaches for Intrusion Detection. Journal of Fusion: Practice and Applications, 13 ( 1 ), 126-134 (Doi : https://doi.org/10.54216/FPA.130110) |
| Chicago | Reem Atassi. "Anomaly Detection in IoT Networks: Machine Learning Approaches for Intrusion Detection." Journal of Fusion: Practice and Applications, 13 no. 1 (2023): 126-134 (Doi : https://doi.org/10.54216/FPA.130110) |
| Harvard | Reem Atassi. (2023). Anomaly Detection in IoT Networks: Machine Learning Approaches for Intrusion Detection. Journal of Fusion: Practice and Applications, 13 ( 1 ), 126-134 (Doi : https://doi.org/10.54216/FPA.130110) |
| Vancouver | Reem Atassi. Anomaly Detection in IoT Networks: Machine Learning Approaches for Intrusion Detection. Journal of Fusion: Practice and Applications, (2023); 13 ( 1 ): 126-134 (Doi : https://doi.org/10.54216/FPA.130110) |
| IEEE | Reem Atassi, Anomaly Detection in IoT Networks: Machine Learning Approaches for Intrusion Detection, Journal of Fusion: Practice and Applications, Vol. 13 , No. 1 , (2023) : 126-134 (Doi : https://doi.org/10.54216/FPA.130110) |