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Title |
Characterizing Geographical Distribution of Tor Node by Local Density Comparison |
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Author |
Ruo Ando, Nguyen Minh Hieu, Pan Haoqian., Yi Liu and Yoshiyasu Takefuji |
| Citation |
Vol. 25 No. 2 pp. 225-230 |
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Abstract |
This study investigates the dynamic distribution of Tor nodes using a density-based Local Outlier Factor (LOF) method. Malicious Tor nodes, characterized by frequent appearance and disappearance, challenge traditional reputation-based approaches, which often require time to stabilize scores and fail to capture localized fluctuations effectively. The research applied LOF to analyze daily geographic distributions of Tor nodes from April to May 2024 and compared its performance with the DBSCAN method. The evaluation included measurements of reputation scores, their variance, and mean during the same period. Findings reveal that LOF outperforms DBSCAN in detecting localized fluctuations and provides faster identification of malicious Tor nodes. This method offers a significant advantage over conventional reputation-based approaches, allowing more accurate and timely monitoring of Tor node activity. Reputation calculations leveraged the AbuseIPDB API, ensuring reliable data for the analysis and validating the proposed methods efficiency. |
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Keywords |
Characterizing Geographical Distribution, Tor Node, Density Comparison |
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