Software-Defined IoT with Machine Learning-Based Enhanced Security
Publication Name
Proceedings - 2023 28th Asia Pacific Conference on Communications, APCC 2023
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.
Open Access Status
This publication is not available as open access
First Page
430
Last Page
435