85 citations · 183 across the 20 of their papers we have counts for
20 papers
Many or Few Samples? Comparing Transfer, Contrastive and Meta-Learning in Encrypted Traffic Classification
Idio Guarino, Chao Wang, Alessandro Finamore +2
The popularity of Deep Learning (DL), coupled with network traffic visibility reduction due to the increased adoption of HTTPS, QUIC and DNS-SEC, re-ignited interest towards Traffi…
Continuous-Time Functional Diffusion Processes
Giulio Franzese, Giulio Corallo, Simone Rossi +3
We introduce Functional Diffusion Processes (FDPs), which generalize score-based diffusion models to infinite-dimensional function spaces. FDPs require a new mathematical framework…
"It's a Match!" -- A Benchmark of Task Affinity Scores for Joint Learning
Raphael Azorin, Massimo Gallo, Alessandro Finamore +2
While the promises of Multi-Task Learning (MTL) are attractive, characterizing the conditions of its success is still an open problem in Deep Learning. Some tasks may benefit from…
Cross-network transferable neural models for WLAN interference estimation
Danilo Marinho Fernandes, Jonatan Krolikowski, Zied Ben Houidi +2
Airtime interference is a key performance indicator for WLANs, measuring, for a given time period, the percentage of time during which a node is forced to wait for other transmissi…
Rare Yet Popular: Evidence and Implications from Labeled Datasets for Network Anomaly Detection
Jose Manuel Navarro, Alexis Huet, Dario Rossi
Anomaly detection research works generally propose algorithms or end-to-end systems that are designed to automatically discover outliers in a dataset or a stream. While literature…
From Design to Deployment of Zero-touch Deep Reinforcement Learning WLANs
Ovidiu Iacoboaiea, Jonatan Krolikowski, Zied Ben Houidi +1
Machine learning (ML) is increasingly used to automate networking tasks, in a paradigm known as zero-touch network and service management (ZSM). In particular, Deep Reinforcement L…
Local Evaluation of Time Series Anomaly Detection Algorithms
Alexis Huet, Jose Manuel Navarro, Dario Rossi
In recent years, specific evaluation metrics for time series anomaly detection algorithms have been developed to handle the limitations of the classical precision and recall. Howev…
How Much is Enough? A Study on Diffusion Times in Score-based Generative Models
Giulio Franzese, Simone Rossi, Lixuan Yang +4
Score-based diffusion models are a class of generative models whose dynamics is described by stochastic differential equations that map noise into data. While recent works have sta…
Neural language models for network configuration: Opportunities and reality check
Zied Ben Houidi, Dario Rossi
Boosted by deep learning, natural language processing (NLP) techniques have recently seen spectacular progress, mainly fueled by breakthroughs both in representation learning with…
Landing AI on Networks: An equipment vendor viewpoint on Autonomous Driving Networks
Dario Rossi, Liang Zhang
The tremendous achievements of Artificial Intelligence (AI) in computer vision, natural language processing, games and robotics, has extended the reach of the AI hype to other fiel…
Quality Monitoring and Assessment of Deployed Deep Learning Models for Network AIOps
Lixuan Yang, Dario Rossi
Artificial Intelligence (AI) has recently attracted a lot of attention, transitioning from research labs to a wide range of successful deployments in many fields, which is particul…
A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification
Kevin Fauvel, Fuxing Chen, Dario Rossi
Traffic classification, i.e. the identification of the type of applications flowing in a network, is a strategic task for numerous activities (e.g., intrusion detection, routing).…
Accelerating Deep Learning Classification with Error-controlled Approximate-key Caching
Alessandro Finamore, James Roberts, Massimo Gallo +1
While Deep Learning (DL) technologies are a promising tool to solve networking problems that map to classification tasks, their computational complexity is still too high with resp…
Neural combinatorial optimization beyond the TSP: Existing architectures under-represent graph structure
Matteo Boffa, Zied Ben Houidi, Jonatan Krolikowski +1
Recent years have witnessed the promise that reinforcement learning, coupled with Graph Neural Network (GNN) architectures, could learn to solve hard combinatorial optimization pro…
Human readable network troubleshooting based on anomaly detection and feature scoring
Jose M. Navarro, Alexis Huet, Dario Rossi
Network troubleshooting is still a heavily human-intensive process. To reduce the time spent by human operators in the diagnosis process, we present a system based on (i) unsupervi…
HURRA! Human readable router anomaly detection
Jose M. Navarro, Dario Rossi
This paper presents HURRA, a system that aims to reduce the time spent by human operators in the process of network troubleshooting. To do so, it comprises two modules that are plu…
Thinkback: Task-SpecificOut-of-Distribution Detection
Lixuan Yang, Dario Rossi
The increased success of Deep Learning (DL) has recently sparked large-scale deployment of DL models in many diverse industry segments. Yet, a crucial weakness of supervised model…
A First Look at Class Incremental Learning in Deep Learning Mobile Traffic Classification
Giampaolo Bovenzi, Lixuan Yang, Alessandro Finamore +4
The recent popularity growth of Deep Learning (DL) re-ignited the interest towards traffic classification, with several studies demonstrating the accuracy of DL-based classifiers t…
Deep Learning and Traffic Classification: Lessons learned from a commercial-grade dataset with hundreds of encrypted and zero-day applications
Lixuan Yang, Alessandro Finamore, Feng Jun +1
The increasing success of Machine Learning (ML) and Deep Learning (DL) has recently re-sparked interest towards traffic classification. While classification of known traffic is a w…
Heterogeneous Data-Aware Federated Learning
Lixuan Yang, Cedric Beliard, Dario Rossi
Federated learning (FL) is an appealing concept to perform distributed training of Neural Networks (NN) while keeping data private. With the industrialization of the FL framework,…