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• W4385302045 abstract "IoT devices deployed on campus and enterprise networks facilitate people's lives and work. However, these devices also bring serious network asset management and security management problems. IoT device identification is the premise to solve these problems. Although current IoT identification methods can identify devices with relatively high accuracy in ideal environments, it is difficult to accurately identify devices in real-world complex environments (e.g., campus networks, enterprise networks). Therefore, we propose to use exact features. To solve the problem of different dimensions of exact features, we creatively model the IoT identification problem as a heterogeneous graph representation learning problem and design a new representation learning algorithm. We are the first to propose an approach to accurately identify IoT devices in real-world complex environments and solve this problem through heterogeneous graphs. The evaluation shows that GraphIoT's macro F1 is on average 13.58% and 12.77% higher than the other methods on two public datasets." @default.
• W4385302045 created "2023-07-28" @default.
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• W4385302045 date "2023-06-19" @default.
• W4385302045 modified "2023-10-16" @default.
• W4385302045 title "GraphIoT: Accurate IoT Identification based on Heterogeneous Graph" @default.
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• W4385302045 doi "https://doi.org/10.1109/iwqos57198.2023.10188710" @default.
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