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New CESNET-TimeSeries26 dataset aids long-term network traffic analysis

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced CESNET-TimeSeries26, a new dataset designed to evaluate network traffic forecasting models over extended periods. The dataset, sourced from the CESNET3 network, spans approximately 4.5 years and includes detailed traffic data, capturing information on IP addresses, institutions, and subnets across various aggregation intervals. This comprehensive dataset aims to serve as a longitudinal benchmark for assessing model adaptability to yearly seasonality and concept drift, which are crucial for effective network management and security monitoring. AI

IMPACT Provides a benchmark for evaluating AI models in network traffic analysis and anomaly detection.

RANK_REASON The item is an academic paper introducing a new dataset for research purposes. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New CESNET-TimeSeries26 dataset aids long-term network traffic analysis

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The item is an academic paper introducing a new dataset for research purposes. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Josef Koumar, Karel Hynek, Kamil Jerabek, Pavel Siska ·

    CESNET-TimeSeries26: A Multi-Year Network Traffic Time Series Dataset for Annual Seasonality, Concept Drift, and Long-Term Anomaly Detection

    arXiv:2610.14799v1 Announce Type: new Abstract: 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 time in order to assess their adaptability to unexpect…

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