HMCGeo: IP Region Prediction Based on Hierarchical Multi-label Classification
This work addresses the need for accurate IP region prediction for applications like location-based services and cybersecurity, representing an incremental improvement over existing methods.
The paper tackles the problem of fine-grained IP geolocation, which suffers from kilometer-level errors in regression-based methods, by proposing HMCGeo, a hierarchical multi-label classification framework that achieves superior performance across multiple geographical granularities on datasets from New York, Los Angeles, and Shanghai.
Fine-grained IP geolocation plays a critical role in applications such as location-based services and cybersecurity. Most existing fine-grained IP geolocation methods are regression-based; however, due to noise in the input data, these methods typically encounter kilometer-level prediction errors and provide incorrect region information for users. To address this issue, this paper proposes a novel hierarchical multi-label classification framework for IP region prediction, named HMCGeo. This framework treats IP geolocation as a hierarchical multi-label classification problem and employs residual connection-based feature extraction and attention prediction units to predict the target host region across multiple geographical granularities. Furthermore, we introduce probabilistic classification loss during training, combining it with hierarchical cross-entropy loss to form a composite loss function. This approach optimizes predictions by utilizing hierarchical constraints between regions at different granularities. IP region prediction experiments on the New York, Los Angeles, and Shanghai datasets demonstrate that HMCGeo achieves superior performance across all geographical granularities, significantly outperforming existing IP geolocation methods.