1 citations · 1 across the 5 of their papers we have counts for
6 papers
CESNET-TimeSeries26: A Multi-Year Network Traffic Time Series Dataset for Annual Seasonality, Concept Drift, and Long-Term Anomaly Detection
Josef Koumar, Karel Hynek, Kamil Jerabek +1
Forecast-based models are fundamental to network traffic management and anomaly detection. However, it is essential to evaluate these forecasting models over extended periods of ti…
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…
Comparative Analysis of Deep Learning Models for Real-World ISP Network Traffic Forecasting
Josef Koumar, Timotej Smoleň, Kamil Jeřábek +1
Accurate network traffic forecasting is essential for Internet Service Providers (ISP) to optimize resources, enhance user experience, and mitigate anomalies. This study evaluates…
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…
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…