363 citations · 1.6k across the 44 of their papers we have counts for
47 papers
Cybersecurity in Power Grids: Standards and Research Challenges
Ferran Bohigas-Daranas, Hamid Latif-Martinez, Nicolas Llorens +4
This paper examines Smart Grid cybersecurity, emphasizing the critical distinctions between IT and OT environments. It analyzes grid architecture, substation threats, and key inter…
Unified Heterogeneous Graph Neural Network solver for Power Flow, Optimal Power Flow and State Estimation
Ferran Bohigas-Daranas, Hamid Latif-Martínez, Eduardo Prieto-Araujo +2
Power Flow (PF), Optimal Power Flow (OPF), and State Estimation (SE) are fundamental problems in power system analysis, but solving them is computationally expensive. Graph Neural…
Power Flow Feasibility Assessment Using Variational Graph Autoencoders
Ferran Bohigas-Daranas, Hamid Latif-Martinez, Eduardo Prieto-Araujo +2
Data-driven methods, including graph neural networks, have been studied for accelerating power flow calculations in recent years, but very little attention has been paid to the sol…
Forecasting Individual NetFlows using a Predictive Masked Graph Autoencoder
Georgios Anyfantis, Pere Barlet-Ros
In this paper, we propose a proof-of-concept Graph Neural Network model that can successfully predict network flow-level traffic (NetFlow) by accurately modelling the graph structu…
From Simulation to Deep Learning: Survey on Network Performance Modeling Approaches
Carlos Güemes-Palau, Miquel Ferriol-Galmés, Jordi Paillisse-Vilanova +2
Network performance modeling is a field that predates early computer networks and the beginning of the Internet. It aims to predict the traffic performance of packet flows in a giv…
AutoGraphAD: Unsupervised network anomaly detection using Variational Graph Autoencoders
Georgios Anyfantis, Pere Barlet-Ros
Network Intrusion Detection Systems (NIDS) are essential tools for detecting network attacks and intrusions. While extensive research has explored the use of supervised Machine Lea…
Bridging the Gap Between Simulated and Real Network Data Using Transfer Learning
Carlos Güemes-Palau, Miquel Ferriol-Galmés, Jordi Paillisse-Vilanova +3
Machine Learning (ML)-based network models provide fast and accurate predictions for complex network behaviors but require substantial training data. Collecting such data from real…
GraphUniverse: Synthetic Graph Generation for Evaluating Inductive Generalization
Louis Van Langendonck, Guillermo Bernárdez, Nina Miolane +1
A fundamental challenge in graph learning is understanding how models generalize to new, unseen graphs. While synthetic benchmarks offer controlled settings for analysis, existing…
Ordered Topological Deep Learning: a Network Modeling Case Study
Guillermo Bernárdez, Miquel Ferriol-Galmés, Carlos Güemes-Palau +4
Computer networks are the foundation of modern digital infrastructure, facilitating global communication and data exchange. As demand for reliable high-bandwidth connectivity grows…
RouteNet-Gauss: Hardware-Enhanced Network Modeling with Machine Learning
Carlos Güemes-Palau, Miquel Ferriol-Galmés, Jordi Paillisse-Vilanova +3
Network simulation is pivotal in network modeling, assisting with tasks ranging from capacity planning to performance estimation. Traditional approaches such as Discrete Event Simu…
Towards a graph-based foundation model for network traffic analysis
Louis Van Langendonck, Ismael Castell-Uroz, Pere Barlet-Ros
Foundation models have shown great promise in various fields of study. A potential application of such models is in computer network traffic analysis, where these models can grasp…
PPT-GNN: A Practical Pre-Trained Spatio-Temporal Graph Neural Network for Network Security
Louis Van Langendonck, Ismael Castell-Uroz, Pere Barlet-Ros
Recent works have demonstrated the potential of Graph Neural Networks (GNN) for network intrusion detection. Despite their advantages, a significant gap persists between real-world…
A first look into Utiq: Next-generation cookies at the ISP level
Ismael Castell-Uroz, Pere Barlet-Ros
Online privacy has become increasingly important in recent years. While third-party cookies have been widely used for years, they have also been criticized for their potential impa…
Circuit Partitioning for Multi-Core Quantum Architectures with Deep Reinforcement Learning
Arnau Pastor, Pau Escofet, Sahar Ben Rached +3
Quantum computing holds immense potential for solving classically intractable problems by leveraging the unique properties of quantum mechanics. The scalability of quantum architec…
Detecting Contextual Network Anomalies with Graph Neural Networks
Hamid Latif-Martínez, José Suárez-Varela, Albert Cabellos-Aparicio +1
Detecting anomalies on network traffic is a complex task due to the massive amount of traffic flows in today's networks, as well as the highly-dynamic nature of traffic over time.…
Atom: Neural Traffic Compression with Spatio-Temporal Graph Neural Networks
Paul Almasan, Krzysztof Rusek, Shihan Xiao +4
Storing network traffic data is key to efficient network management; however, it is becoming more challenging and costly due to the ever-increasing data transmission rates, traffic…
Building a Graph-based Deep Learning network model from captured traffic traces
Carlos Güemes-Palau, Miquel Ferriol Galmés, Albert Cabellos-Aparicio +1
Currently the state of the art network models are based or depend on Discrete Event Simulation (DES). While DES is highly accurate, it is also computationally costly and cumbersome…
Enhancing 5G Radio Planning with Graph Representations and Deep Learning
Paul Almasan, José Suárez-Varela, Andra Lutu +2
The roll out of new mobile network generations poses hard challenges due to various factors such as cost-benefit tradeoffs, existing infrastructure, and new technology aspects. In…
GraphCC: A Practical Graph Learning-based Approach to Congestion Control in Datacenters
Guillermo Bernárdez, José Suárez-Varela, Xiang Shi +4
Congestion Control (CC) plays a fundamental role in optimizing traffic in Data Center Networks (DCN). Currently, DCNs mainly implement two main CC protocols: DCTCP and DCQCN. Both…
Topological Graph Signal Compression
Guillermo Bernárdez, Lev Telyatnikov, Eduard Alarcón +3
Recently emerged Topological Deep Learning (TDL) methods aim to extend current Graph Neural Networks (GNN) by naturally processing higher-order interactions, going beyond the pairw…
MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering
Guillermo Bernárdez, José Suárez-Varela, Albert López +5
Current trends in networking propose the use of Machine Learning (ML) for a wide variety of network optimization tasks. As such, many efforts have been made to produce ML-based sol…
ASTrack: Automatic Detection and Removal of Web Tracking Code with Minimal Functionality Loss
Ismael Castell-Uroz, Kensuke Fukuda, Pere Barlet-Ros
Recent advances in web technologies make it more difficult than ever to detect and block web tracking systems. In this work, we propose ASTrack, a novel approach to web tracking de…
Leveraging Spatial and Temporal Correlations for Network Traffic Compression
Paul Almasan, Krzysztof Rusek, Shihan Xiao +4
The deployment of modern network applications is increasing the network size and traffic volumes at an unprecedented pace. Storing network-related information (e.g., traffic traces…
Proximal Policy Optimization with Graph Neural Networks for Optimal Power Flow
Ángela López-Cardona, Guillermo Bernárdez, Pere Barlet-Ros +1
Optimal Power Flow (OPF) is a very traditional research area within the power systems field that seeks for the optimal operation point of electric power plants, and which needs to…