HMCGeo: IP Region Prediction Based on Hierarchical Multi-label Classification

TL;DR

HMCGeo introduces a hierarchical multi-label classification framework with residual connections and attention units for more accurate IP region prediction across multiple granularities, outperforming existing methods.

Contribution

This paper presents a novel hierarchical multi-label classification approach with probabilistic loss for IP geolocation, addressing noise issues in regression-based methods.

Findings

HMCGeo outperforms existing IP geolocation methods across datasets.

The hierarchical loss improves prediction accuracy at multiple granularities.

Residual and attention modules enhance feature extraction and prediction quality.

Abstract

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.