Deep Learning Based Intrusion Detection System for Network Security in IoT System
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119-138
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10.58578/ijemt.v3i1.4539
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Authors:Jennifer E Joseph1, Ngozi Tracy Aleke2, Onyinyechukwu Prisca Onyeanisi3
Copyright :Authors retain copyright and grant the journal right of first publication.
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Abstract
The Internet of Things (IoT) has grown rapidly, leading to unparalleled connectivity and vast amounts of data. Anomaly detection plays a crucial role in identifying unusual behavior that deviates from the system's normal operation, enabling the swift detection and resolution of these anomalies. The integration of artificial intelligence (AI) with IoT significantly improves the effectiveness of anomaly detection, enhancing the performance, dependability, and security of IoT systems. AI-powered anomaly detection methods can recognize a wide array of threats within IoT environments, such as brute force attacks, buffer overflows, injection attacks, replay attacks, Distributed Denial of Service (DDoS) attacks, SQL injection, and backdoor threats. Intelligent Intrusion Detection Systems (IDS) are essential for IoT devices, as they help monitor networks for intrusions or anomalies. With the increasing adoption of IoT across various industries and its extensive attack surface, it offers more opportunities for malicious actors to exploit vulnerabilities. This paper reviews existing literature on anomaly detection in IoT systems using machine learning and deep learning approaches. It discusses the challenges associated with detecting intrusions and anomalies in IoT environments, emphasizing the rise in attacks. Recent advancements in machine learning and deep learning techniques for anomaly detection in IoT networks are examined, and the paper concludes that there is a need for further enhancement of these systems through the use of diverse datasets, real-time testing, and scalability improvements.
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References
Alcaide, J., Rebollo, R., & Cano, A. (2019). Anomaly-based intrusion detection systems for IoT: A comparative study. International Journal of Network Security, 21(1), 110-118.
Berger, C.; Eichhammer, P.; Reiser, H.P.; Domaschka, J.; Hauck, F.J.; Habiger, G. A Survey on Resilience in the IoT: Taxonomy, Classification, and Discussion of Resilience Mechanisms. ACM Comput. Surv. 2022, 54, 147
Cheng, L., Zhang, X., & Liu, T. (2021). Ensemble-based anomaly detection in IoT systems. Journal of Network and Computer Applications, 45, 136-142. https://doi.org/10.1016/j.jnca.2021.102
Ghafir, I.; Husak, M.; Prenosil, V. A survey on intrusion detection and prevention systems. In Proceedings of the Student Conference Zvule, IEEE/UREL, Brno University of Technology, Zvůle, Czech Republic, 25–27 August 2014; Volume 1014
Hao, Z., Zheng, M., & Wei, L. (2020). Real-time anomaly detection in IoT systems using deep learning models. IEEE Access, 8, 43619-43627. https://doi.org/10.1109/ACCESS.2020.2971378
Hassan, H., Alam, M., & Chang, T. (2021). A hybrid intrusion detection framework for secure IoT networks. IEEE Transactions on Industrial Informatics, 17(4), 2746-2755. https://doi.org/10.1109/TII.2020.2979569
He, X., Zhang, Q., & Song, Y. (2020). Deep learning for IoT anomaly detection: Challenges and opportunities. IEEE Access, 8, 100204-100214.
Hikal, N.A.; Elgayar, M. Enhancing IoT botnets attack detection using machine learning-IDS and ensemble data preprocessing technique. In Internet of Things—Applications and Future; Springer: Berlin/Heidelberg, Germany, 2020; pp. 89–102.
Jeyanthi, D.; Indrani, B. Intrusion Detection System Intensive on Securing IoT Networking Environment Based on Machine Learning Strategy. In Intelligent Data Communication Technologies and Internet of Things; Springer: Berlin/Heidelberg, Germany, 2022; pp. 139–157.
Jiang, S., Li, X., & Zhou, W. (2021). A deep learning-based approach for anomaly detection in IoT networks. IEEE Transactions on Industrial Informatics, 17(7), 4770-4779.
Li, Y., Wu, S., & Liu, W. (2020). A hybrid intrusion detection system for IoT networks using machine learning and signature-based methods. Security and Privacy, 3(5), e157.
Lin, K.; Xu, X.; Xiao, F. MFFusion: A Multi-level Features Fusion Model for Malicious Traffic Detection based on Deep Learning. Comput. Netw. 2022, 202, 108658.
Liu, X., Guo, Y., & Zhang, P. (2020). Feature selection and data preprocessing for anomaly detection in IoT networks. Sensors, 20(6), 1595. https://doi.org/10.3390/s20061595
Liu, Y., Zhang, H., & Xu, Z. (2021). Blockchain-based intrusion detection for IoT: A survey. IEEE Access, 9, 118528-118539. https://doi.org/10.1109/ACCESS.2021.3051792
Liu, Y., Zhang, H., & Xu, Z. (2021). Edge computing-based deep learning for anomaly detection in IoT networks. IEEE Transactions on Industrial Electronics, 68(5), 4221-4230.
Mishra, N.; Pandya, S. Internet of things applications, security challenges, attacks, intrusion detection, and future visions: A systematic review. IEEE Access 2021, 9, 59353–59377.
Nguyen, D., Lee, J., & Ryu, K. (2021). Anomaly detection for IoT using machine learning and deep learning. Sensors, 21(9), 2975.
Panigrahi, R.; Borah, S.; Bhoi, A.K.; Mallick, P.K. Intrusion detection systems (IDS)—An overview with a generalized framework. In Proceedings of the Cognitive Informatics and Soft Computing, Balasore, India, 12–13 December 2020; pp. 107–117.
Rafique, S. H., Abdallah, A., Musa, N. S., & Murugan, T. (2024). Machine learning and deep learning techniques for internet of things network anomaly detection—current research trends. Sensors, 24(6), 19,68
Raza, S., Wallgren, L., & Voigt, T. (2018). Secure and scalable intrusion detection system for IoT networks. IEEE Internet of Things Journal, 5(3), 2182-2193.
Sharma, A., Jain, R., & Srivastava, A. (2020). Transfer learning for anomaly detection in IoT systems. Journal of Computer Networks and Communications, 2020, 1-10.
Sharma, A., Jain, R., & Srivastava, A. (2020). Transfer learning for anomaly detection in IoT systems. Journal of Computer Networks and Communications, 2020, 1-10. https://doi.org/10.1155/2020/3983172
Thakkar, A.; Lohiya, R. A review on machine learning and deep learning perspectives of IDS for IoT: Recent updates, security issues, and challenges. Arch. Comput. Methods Eng. 2021, 28, 3211–3243.
Wang, X., Sun, Y., & Liu, X. (2021). Privacy-preserving anomaly detection in IoT networks. IEEE Internet of Things Journal, 8(5), 3899-3908. https://doi.org/10.1109/JIOT.2020.3031487
Yousef, Z.; Barhoumi, H.G.W. Four-layer Architecture for IoT Security in Fog Network. In Proceedings of the 2023 10th International Conference on Internet of Things: Systems, Management and Security, IOTSMS 2023, San Antonio, TX, USA, 23–25 October 2023; pp. 59–65
Zhang, Q., Cheng, L., & Xu, X. (2022). Temporal anomaly detection in IoT networks using recurrent neural networks. Neural Computing and Applications, 34(6), 4367-4376.
Zhang, Z., Xu, C., & Liu, F. (2022). Federated learning-based anomaly detection for IoT networks. IEEE Transactions on Industrial Electronics, 69(7), 7115-7123. https://doi.org/10.1109/TIE.2022.3149538
Zhou, Z., Yu, L., & Zhang, Y. (2021). A survey on machine learning techniques for anomaly detection in IoT environments. Future Generation Computer Systems, 115, 31-42.