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- W4308269145 abstract "Rule-based fine-grained IP geolocation methods are hard to generalize in computer networks which do not follow hypothetical rules. Recently, deep learning methods, like multi-layer perceptron (MLP), are tried to increase generalization capabilities. However, MLP is not so suitable for graph-structured data like networks. MLP treats IP addresses as isolated instances and ignores the connection information, which limits geolocation accuracy. In this work, we research how to increase the generalization capability with an emerging graph deep learning method - Graph Neural Network (GNN). First, IP geolocation is re-formulated as an attributed graph node regression problem. Then, we propose a GNN-based IP geolocation framework named GNN-Geo. GNN-Geo consists of a preprocessor, an encoder, messaging passing (MP) layers and a decoder. The preprocessor and encoder transform measurement data into the initial node embeddings. MP layers refine the initial node embeddings by modeling the connection information. The decoder maps the refined embeddings to nodes' locations and relieves the convergence problem of GNN by considering prior knowledge. The experiments in different real-world datasets show: the proposed GNN-Geo outperforms the state-of-art rule-based and learning-based baselines on all datasets w.r.t median error distance by 16% to 28%. This work verifies the great potential of GNN for fine-grained IP geolocation." @default.
- W4308269145 created "2022-11-10" @default.
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- W4308269145 date "2021-12-18" @default.
- W4308269145 modified "2023-10-18" @default.
- W4308269145 title "GNN-Geo: A Graph Neural Network-based Fine-grained IP geolocation Framework" @default.
- W4308269145 doi "https://doi.org/10.48550/arxiv.2112.10767" @default.
- W4308269145 hasPublicationYear "2021" @default.
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