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This articles surveys the existing literature on the methods currently used by web services to track the user online as well as their purposes, implications, and possible user's defenses. A significant majority of reviewed articles and web resources are from years 2012–2014. Privacy seems to be the Achilles' heel of today's web. Web services make continuous efforts to obtain as much information as they can about the things we search, the sites we visit, the people with who we contact, and the products we buy. Tracking is usually performed for commercial purposes. We present 5 main groups of methods used for user tracking, which are based on sessions, client storage, client cache, fingerprinting, or yet other approaches. A special focus is placed on mechanisms that use web caches, operational caches, and fingerprinting, as they are usually very rich in terms of using various creative methodologies. We also show how the users can be identified on the web and associated with their real names, e-mail addresses, phone numbers, or even street addresses. We show why tracking is being used and its possible implications for the users. For example, we describe recent cases of price discrimination, assessing financial credibility, determining insurance coverage, government surveillance, and identity theft. For each of the tracking methods, we present possible defenses. Some of them are specific to a particular tracking approach, while others are more universal (block more than one threat) and they are discussed separately. Apart from describing the methods and tools used for keeping the personal data away from being tracked, we also present several tools that were used for research purposes – their main goal is to discover how and by which entity the users are being tracked on their desktop computers or smartphones, provide this information to the users, and visualize it in an accessible and easy to follow way. Finally, we present the currently proposed future approaches to track the user and show that they can potentially pose significant threats to the users' privacy. ; Postprint (published version)

This articles surveys the existing literature on the methods currently used by web services to track the user online as well as their purposes, implications, and possible user's defenses. A significant majority of reviewed articles and web resources are from years 2012–2014. Privacy seems to be the Achilles' heel of today's web. Web services make continuous efforts to obtain as much information as they can about the things we search, the sites we visit, the people with who we contact, and the products we buy. Tracking is usually performed for commercial purposes. We present 5 main groups of methods used for user tracking, which are based on sessions, client storage, client cache, fingerprinting, or yet other approaches. A special focus is placed on mechanisms that use web caches, operational caches, and fingerprinting, as they are usually very rich in terms of using various creative methodologies. We also show how the users can be identified on the web and associated with their real names, e-mail addresses, phone numbers, or even street addresses. We show why tracking is being used and its possible implications for the users. For example, we describe recent cases of price discrimination, assessing financial credibility, determining insurance coverage, government surveillance, and identity theft. For each of the tracking methods, we present possible defenses. Some of them are specific to a particular tracking approach, while others are more universal (block more than one threat) and they are discussed separately. Apart from describing the methods and tools used for keeping the personal data away from being tracked, we also present several tools that were used for research purposes – their main goal is to discover how and by which entity the users are being tracked on their desktop computers or smartphones, provide this information to the users, and visualize it in an accessible and easy to follow way. Finally, we present the currently proposed future approaches to track the user and show that they can potentially pose significant threats to the users' privacy. ; Postprint (published version)

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. ; Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks. However, we still lack functional network models able to produce accurate predictions of Key Performance Indicators (KPI) such as delay, jitter or loss at limited cost. In this paper we propose RouteNet, a novel network model based on Graph Neural Network (GNN) that is able to understand the complex relationship between topology, routing, and input traffic to produce accurate estimates of the per-source/destination per-packet delay distribution and loss. RouteNet leverages the ability of GNNs to learn and model graph-structured information and as a result, our model is able to generalize over arbitrary topologies, routing schemes and traffic intensity. In our evaluation, we show that RouteNet is able to predict accurately the delay distribution (mean delay and jitter) and loss even in topologies, routing and traffic unseen in the training (worst case MRE = 15.4%). Also, we present several use cases where we leverage the KPI predictions of our GNN model to achieve efficient routing optimization and network planning. ; This work was supported in part by the Polish Ministryof Science and Higher Education with the subvention funds of the Facultyof Computer Science, Electronics and Telecommunications, AGH University,in part by the Spanish MINECO under Contract TEC2017-90034-C2-1-R(ALLIANCE), in part by the Catalan Institution for Research and AdvancedStudies (ICREA) and the FI-AGAUR Grant by the Catalan Government, andin part by PL-Grid Infrastructure. ; Peer Reviewed ; Postprint (author's final draft)

Recent years have seen the vast potential of graph neural networks (GNN) in many fields where data is structured as graphs (e.g., chemistry, recommender systems). In particular, GNNs are becoming increasingly popular in the field of networking, as graphs are intrinsically present at many levels (e.g., topology, routing). The main novelty of GNNs is their ability to generalize to other networks unseen during training, which is an essential feature for developing practical machine learning (ML) solutions for networking. However, implementing a functional GNN prototype is currently a cumbersome task that requires strong skills in neural network programming. This poses an important barrier to network engineers that often do not have the necessary ML expertise. In this article, we present IGNNITION, a novel open source framework that enables fast prototyping of GNNs for networking systems. IGNNITION is based on an intuitive high-level abstraction that hides the complexity behind GNNs, while still offering great flexibility to build custom GNN architectures. To showcase the versatility and performance of this framework, we implement two state-of-the-art GNN models applied to different networking use cases. Our results show that the GNN models produced by IGNNITION are equivalent in terms of accuracy and performance to their native implementations in TensorFlow. ; This open source project has received funding from the European Union's Horizon 2020 research and innovation programme within the framework of the NGI-POINTER Project funded under grant agreement No. 871528. This article reflects only the author's view; the European Commission is not responsible for any use that may be made of the information it contains. The work was also supported by the Spanish MINECO under contract TEC2017-90034-C2-1-R (ALLIANCE) and the Catalan Institution for Research and Advanced Studies (ICREA) ; Peer Reviewed ; Postprint (author's final draft)

Graph Neural Networks (GNN) have recently exploded in the Machine Learning area as a novel technique for modeling graph-structured data. This makes them especially suitable for applications in the networking field, as communication networks inherently comprise graphs at many levels (e.g., topology, routing, user connections). In this demo, we will present IGNNITION, an open-source framework for fast prototyping of GNNs applied to communication networks. This framework is especially designed for network engineers and/or researchers with limited background on neural network programming. IGNNITION comprises a set of tools and functionalities that eases and accelerates the whole implementation process, from the design of a GNN model, to its training, evaluation, debugging, and integration into larger network applications. In the demo, we will show how a user can implement a complex GNN model applied to network performance modeling (RouteNet), following three simple steps. ; This open-source project has received funding from the European Union's Horizon 2020 research and innovation programme within the framework of the NGI-POINTER Project funded under grant agreement No. 871528. This article reflects only the authors' view; the EC is not responsible for any use that may be made of the information it contains. The work was also supported by the Spanish MINECO under contract TEC2017-90034-C2-1-R (ALLIANCE) and the Catalan Institution for Research and Advanced Studies (ICREA). ; Peer Reviewed ; Postprint (author's final draft)

During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments. This poses the need to count on new generations of students, researchers and practitioners with a solid background in ML applied to networks. During 2020, the International Telecommunication Union (ITU) has organized the "ITU AI/ML in 5G challenge", an open global competition that has introduced to a broad audience some of the current main challenges in ML for networks. This large-scale initiative has gathered 23 different challenges proposed by network operators, equipment manufacturers and academia, and has attracted a total of 1300+ participants from 60+ countries. This paper narrates our experience organizing one of the proposed challenges: the "Graph Neural Networking Challenge 2020". We describe the problem presented to participants, the tools and resources provided, some organization aspects and participation statistics, an outline of the top-3 awarded solutions, and a summary with some lessons learned during all this journey. As a result, this challenge leaves a curated set of educational resources openly available to anyone interested in the topic. ; This work has received funding from the European Union's H2020 research and innovation programme within the framework of the NGI-POINTER Project funded under grant agreement No. 871528. This paper reflects only the authors' view; the European Commission is not responsible for any use that may be made of the information it contains. This work was also supported by the Spanish MINECO under contract TEC2017-90034-C2-1-R (ALLIANCE), the Catalan Institution for Research and Advanced Studies (ICREA), and by FI-AGAUR grant by the Catalan Government. Salzburg Research is grateful for the support by the WISS 2025 (Science and Innovation Strategy Salzburg 2025) project "IDALab Salzburg" (20204-WISS/225/197-2019 and 20102-F1901166-KZP) and the 5G-AI-MLab by the Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK) and the Austrian state Salzburg. ; Peer Reviewed ; Article escrit per 24 autors/autores: José Suárez-Varela (1), Miquel Ferriol-Galmés (1), Albert López (1), Paul Almasan (1), Guillermo Bernárdez (1), David Pujol-Perich (1), Krzysztof Rusek (1,2), Loïck Bonniot (3, 4), Christoph Neumann (3), François Schnitzler (3), François Taïani (4), Martin Happ (5, 6), Christian Maier (5), Jia Lei Du (5), Matthias Herlich (5), Peter Dorfinger (5), Nick Vincent Hainke (7), Stefan Venz (7), Johannes Wegener (7), Henrike Wissing (7), Bo Wu (8), Shihan Xiao (8), Pere Barlet-Ros (1), Albert Cabellos-Aparicio (1). 1- Barcelona Neural Networking center, Universitat Politècnica de Catalunya, Spain. 2- AGH University of Science and Technology, Department of Telecommunications, Poland. 3- InterDigital, France. 4- Univ. Rennes, Inria, CNRS, IRISA, France. 5- Salzburg Research Forschungsgesellschaft mbH, Austria. 6- IDA Lab, University of Salzburg, Austria. 7- Fraunhofer HHI, Germany. 8- Network Technology Lab., Huawei Technologies Co., Ltd., China. ; Postprint (author's final draft)