32 citations · 42 across the 7 of their papers we have counts for
7 papers
When Simple Model Just Works: Is Network Traffic Classification in Crisis?
Kamil Jerabek, Jan Luxemburk, Richard Plny +3
Machine learning has been applied to network traffic classification (TC) for over two decades. While early efforts used shallow models, the latter 2010s saw a shift toward complex…
Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success
Jan Luxemburk, Karel Hynek, Richard Plný +1
Encrypted traffic classification (TC) methods must adapt to new protocols and extensions as well as to advancements in other machine learning fields. In this paper, we adopt a tran…
CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting
Josef Koumar, Karel Hynek, Tomáš Čejka +1
Anomaly detection in network traffic is crucial for maintaining the security of computer networks and identifying malicious activities. One of the primary approaches to anomaly det…
DataZoo: Streamlining Traffic Classification Experiments
Jan Luxemburk, Karel Hynek
The machine learning communities, such as those around computer vision or natural language processing, have developed numerous supportive tools and benchmark datasets to accelerate…
NetTiSA: Extended IP Flow with Time-series Features for Universal Bandwidth-constrained High-speed Network Traffic Classification
Josef Koumar, Karel Hynek, Jaroslav Pešek +1
Network traffic monitoring based on IP Flows is a standard monitoring approach that can be deployed to various network infrastructures, even the large IPS-based networks connecting…
Network Traffic Classification based on Single Flow Time Series Analysis
Josef Koumar, Karel Hynek, Tomáš Čejka
Network traffic monitoring using IP flows is used to handle the current challenge of analyzing encrypted network communication. Nevertheless, the packet aggregation into flow recor…