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Title	Software-Defined Iot With Machine Learning-Based Enhanced Security
ID_Doc	52188
Authors	Husnain A.; Nguyen C.; Le N.T.
Year	2023
Published	Proceedings - 2023 28th Asia Pacific Conference on Communications, APCC 2023
DOI	http://dx.doi.org/10.1109/APCC60132.2023.10460701
Abstract	The widespread adoption of IoT devices has revolutionized multiple sectors, including healthcare, military, agriculture, and smart cities. This surge in IoT-generated data raises significant security concerns, necessitating efficient strategies for large-scale data analysis to safeguard IoT devices. Existing research has explored the fusion of Software-Defined Networking (SDN) and machine learning (ML), particularly flow-based monitoring, for intrusion detection. However, as IoT data volumes grow, challenges such as scalability, adaptability to new attack vectors, and resource-intensive monitoring persist. Our solution combines SD-IoT and ML to enhance IoT network security. By isolating virtual networks based on device characteristics, we improve intrusion detection efficiency and facilitate research on emerging threats. We present a real-world implementation, demonstrating a scalable and robust ML-based security for SD-IoT system. © 2023 IEEE.
Author Keywords	IoT Security; Machine learning; Network Isolation; OpenFlow statistics; Software-defined IoT

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