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ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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Dataset of RTT latency internet measurements using Ripe Atlas anchor nodes in Europe.

Authors: Miguel Ángel Ortega-Velázquez; Maria Dolores Cano-Baños; Pilar Manzanares-López; Antonio J. Jara; Alejandro Santos, Martínez-Sala;

Dataset of RTT latency internet measurements using Ripe Atlas anchor nodes in Europe.

Abstract

This dataset contains real-world latency measurements in internet collected from a distributed network of probing nodes in Europe, designed to enhance IP geolocation accuracy through machine learning techniques. It includes two separate datasets: a Learning Dataset for model training and a Validation Dataset for performance evaluation. Scenario Description The dataset comprises Round-Trip Time (RTT) latency internet measurements in Europe from different geographically distributed probing nodes (Monitors) to target IP addresses. The geolocation information (latitude/longitude) is provided as ground truth for both the Landmarks (training targets) and the Target Nodes (validation targets) to evaluate model accuracy. As a proof-of-concept, it is used the well-known Ripe Atlas anchor nodes inside Europe to act both as landmarks and target nodes (https://atlas.ripe.net/anchors/). Dataset Structure Each dataset contains multiple rows, where each row represents a RTT fingerprint vector consisting of latency measurements from multiple Monitors to a given target. 1) Learning Dataset (LearningDataset_RTT_RipeAtlasEU.csv) Monitors deployed: 4 (distributed across different geographical locations). Targets: Known geographical locations (Landmarks), used for training. Columns: measure_id: Unique identifier for each measurement. anchor_id: ID of the target node to be geolocated. dst_ip: IP address of the target node. init_time: Timestamp of the measurement. latency_m1 - latency_m4: RTT fingerprint vector consisting of latency measurements from 4 different Monitors. latitude, longitude: Ground truth geolocation of the target node. 2) Validation Dataset (ValidationDataset_RTT_RipeAtlasEU.csv) Monitors deployed: 4 (same as Learning Dataset). Targets: IPs used to evaluate model performance. Their actual locations are known but treated as unknown during inference. Columns: measure_id: Unique identifier for each measurement. anchor_id: ID of the target node to be geolocated. dst_ip: IP address of the target node. init_time: Timestamp of the measurement. latency_m1 - latency_m4: RTT fingerprint vector consisting of latency measurements from 4 different Monitors. latitude_anchor, longitude_anchor: Ground truth geolocation of the target node, used to evaluate model accuracy (not used as an input for models). 

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Keywords

Cybersecurity, IP geolocation, Latency, Machine Learning, RIPE Atlas

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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