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CESNET-TimeSeries24:用于网络流量异常检测和预测的时间序列数据集
Josef Koumar1,2, Karel Hynek3,4, Tomáš Čejka3,4
1CESNET, Generála Píky 430/26, 160 00, Prague 6, Czech Republic. josef.koumar@fit.cvut.cz.
Scientific data
|February 26, 2025
概括
本研究引入了一个大规模的网络流量数据集,以改善异常检测. 该数据集能够对基于预测的网络安全方法进行现实的评估.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 数据科学数据科学数据科学
背景情况:
- 在网络流量中检测异常对于网络安全至关重要.
- 基于预测的方法很常见,但缺乏广泛的真实世界数据集来验证.
- 现有的数据集可能会高估算法性能,从而产生错误的进步感.
研究的目的:
- 为了解决缺乏全面的现实世界的网络流量数据集.
- 为开发和评估预测和异常检测模型提供具有挑战性的数据集.
- 为了使在ISP环境中对异常检测算法性能进行更现实的评估.
主要方法:
- 从CESNET3网络收集了40周的网络流量数据.
- 包括来自275,000个活跃IP地址的流量,反映出ISP网络行为的多样性.
- 数据集捕捉了模型开发和评估的现实变化.
主要成果:
- 现在可以获得一个全面的数据集,反映现实世界的ISP网络变化.
- 该数据集有助于开发更强大的预测和异常检测模型.
- 能够更接近网络安全的实际部署场景的评估.
结论:
- 引入的数据集对于推进异常检测研究至关重要.
- 它可以更准确地评估基于预测的安全方法的性能.
- 促进现实的网络安全解决方案的开发和部署.
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