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

新的CESNET-TimeSeries26数据集有助于长期网络流量分析

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员推出了一款名为CESNET-TimeSeries26的新数据集，旨在评估网络流量预测模型在较长时期的表现。该数据集来源于CESNET3网络，跨度约4.5年，包含详细的流量数据，涵盖了IP地址、机构和子网信息，并支持多种聚合间隔。该综合数据集旨在作为纵向基准，用于评估模型对年度季节性和概念漂移的适应性，这对于有效的网络管理和安全监控至关重要。 AI

影响为评估人工智能模型在网络流量分析和异常检测方面的能力提供了基准。

排序理由该条目是一篇介绍新数据集以供研究目的的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Signal score: 16 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该条目是一篇介绍新数据集以供研究目的的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Josef Koumar, Karel Hynek, Kamil Jerabek, Pavel Siska · 2026-10-15 04:00

CESNET-TimeSeries26：用于年度季节性、概念漂移和长期异常检测的多年网络流量时间序列数据集

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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报道来源 [1]

CESNET-TimeSeries26：用于年度季节性、概念漂移和长期异常检测的多年网络流量时间序列数据集

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