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大流-NIDS:大数据环境中的网络入侵检测大型数据集
Mohammed Borhan Uddin1,2, Mohammad Shamsul Arefin1, M M Musharaf Hussain3
1Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chattogram 4349, Bangladesh.
Data in brief
|February 20, 2026
概括
BigFlow-NIDS是一个用于网络入侵检测研究的大数据集,包含数百万个流程和攻击类型. 它的Parquet格式显著加快了用于大数据分析的数据加载速度.
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:
- 网络入侵检测系统 (NIDS) 对网络安全至关重要.
- 大数据环境给传统的NIDS研究带来了挑战.
- 现有的数据集可能缺乏现代威胁所需的规模和细节.
研究的目的:
- 介绍BigFlow-NIDS,这是一个基于NetFlow的大规模数据集.
- 促进大数据环境中的网络入侵检测研究.
- 支持可扩展,对时间有意识的NIDS的发展.
主要方法:
- 收集并策划了66 935 021个网络流量的数据集.
- 包括55个流动属性和32个细粒度攻击类别.
- 在CSV和Parquet中格式化数据,以满足各种分析需求.
主要成果:
- 与CSV相比,Parquet格式显示数据加载速度明显更快 (27.35s vs 920.82s).
- 数据集包括3660万个良性流和3030万个攻击流,突出显示了阶级不平衡.
- 进行了基线分析和异常检测实验.
结论:
- 大流-NIDS是大数据NIDS研究的宝贵资源.
- 像Parquet这样的柱状存储格式对于高效处理大型NIDS数据集至关重要.
- 该数据集使可扩展和时间感知入侵检测系统的评估成为可能.
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