Trust the Source: A latency-based machine learning approach to accurate IP geolocation in internet

By /

Project publication · Computer Networks

Summary

The article proposes an active IP geolocation system that uses RTT measurements from distributed monitors and machine-learning models to estimate the position of Internet nodes.

Arquitectura del sistema de geolocalización IP basado en latencia y aprendizaje automático
Figure from the original article. Source: Computer Networks/Elsevier, reproduced without modification under CC BY-NC-ND 4.0.

Authors: Miguel A. Ortega-Velázquez, Alejandro S. Martínez-Sala, Pilar Manzanares-López, Maria-Dolores Cano, Antonio J. Jara

Journal: Computer Networks

DOI: 10.1016/j.comnet.2025.111721

Funding: PID2023-148214OB-C21.

UPCT Repository: http://hdl.handle.net/10317/21389

Bibliometric indicators

JCR quartileQ1
Impact factor4.6
Google Scholar citations0
JCR area
Computer Science, Information Systems
JCR year
2024
Scholar query
21 May 2026

Main contribution

The contribution combines a Europe-wide measurement campaign with KNN and MLP models to improve accuracy over constraint-based methods. The system targets bounded-latency results and sufficient precision for cybersecurity applications.

Link with BoND1

Although the use case is IP geolocation, the work contributes to BoND1 with a network-observability methodology based on measurements, machine learning and experimental validation, useful for IoT infrastructures and B6G services where traffic origin and trust matter.

DOI UPCT Repository
Scroll al inicio