Artificial intelligence (AI) methods and tools for improving network monitoring practices

The Internet monitoring infrastructure provided publicly by RIPE NCC, RouteViews, and other organizations, is invaluable for network operators, who use it to monitor their Internet routes, detect routing events that affect their traffic, troubleshoot their networks, discover the topology and design their policies, etc.

However, it is well known that the current infrastructure is not uniformly deployed, and this may limit some measurement efforts (e.g., regions with poor coverage) or lead to biased findings (e.g,. over/under-estimate impact of a route event depending on location).

The goal of the AI4NetMon project is to study limitations and biases, and develop tools and strategies for improving the Internet monitoring infrastructure and practices, by addressing two key questions:

Do you use the Internet monitoring and measurement infrastructure? Participate in our online survey, and help us to identify and fix biases!


To answer the research questions AI4NetMon will be based on a data-driven approach, leveraging on rich heterogeneous network data and state-of-the-art ML/AI methods.

The project will develop a set of open-source tools, aimed to be used by Internet operators, researchers, and practitioners (without requiring expertise in ML/AI). The AI4NetMon tools will receive as input the available network data and provide as output answers to the key questions (e.g., potential locations for new monitor deployments, monitoring selection strategies, unbiased estimates for a set of measurements).

Specifically the project objectives are to:

Identify the key use cases for monitoring infrastructure

Which are the best locations to deploy monitors? What is the best set of probes to conduct measurements from? ...There is not a single answer to these questions! The best measurement strategy depends on what one wants to measure; e.g., a best set of monitors for detecting an outage, may be different than the best set of monitors for estimating an anycast catchment. Since a global answer does not exist, we need to provide answers per case. AI4NetMon will identify the use cases of higher importance to network operators, and for which the monitoring infrastructure is more frequently used.

Quantify the bias and efficiency of the existing infrastructure and potential for improvement

What is the accuracy, coverage, and bias of the existing monitoring infrastructure (for a certain use case)? What would be the best achievable performance? Is there a margin for improvement? How much? Building upon preliminary findings (paper), AI4NetMon aims to answer these questions through theory, measurements, and experiments.

Develop open-source tools for efficient Internet monitoring

AI4NetMon will develop AI methods and tools to (i) identify the best locations (e.g., ASes, IXPs, geographical regions) to deploy new infrastructure, (ii) select set of vantage points from the existing infrastructure for lightweight and efficient measurements (e.g., subset of Atlas probes or route collectors), and (iii) unbias estimations from existing measurements. The AI4NetMon tools will collect data from various heterogeneous data sources and aggregate them in a common dataframe, which will feed state-of-the-art AI that will provide recommendations, predictions, inferences, etc., for the desired monitoring objectives.



Pavlos Sermpezis, Vasileios Kotronis, Konstantinos Arakadakis, Athena Vakali "Estimating the Impact of BGP Prefix Hijacking", in IFIP Networking conference, 2021.
[ PDF] [ Presentation ] [ Code ]


AI4NetMon [ github repo ]


Pavlos Sermpezis

Pavlos Sermpezis
Aristotle University of Thessaloniki, Greece

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Christos Georgitsis

Christos Georgitsis
Aristotle University of Thessaloniki, Greece

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Athena Vakali

Athena Vakali
Aristotle University of Thessaloniki, Greece

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Contact us at: sermpezis [at] csd [dot] auth [dot] gr (Pavlos Sermpezis)


AI4NetMon is funded by RIPE NCC, through the RACI project funding 2021 program.