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