85 citations · 183 across the 20 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…
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…