网络安全局势意识的时间序列数据集
Richard Staňa1, Patrik Pekarčík1, Pavol Sokol1
1Faculty of Science, Institute of Computer Science, Pavol Jozef Šafárik University in Košice, Slovakia.
Data in brief
|January 21, 2026
概括
一个新的,大型的网络安全数据集现在可用于网络安全情境意识 (NSSA) 研究. 这一数据集来自数十亿个警报,解决了现有NSSA数据的局限性,使先进的机器学习模型开发成为可能.
科学领域:
- 网络安全 网络安全
- 网络安全 网络安全
- 数据科学数据科学数据科学
背景情况:
- 现有的网络安全情境意识 (NSSA) 数据集往往过时,小或无法访问.
- 这限制了网络安全中先进的机器学习模型的开发和验证.
- 对于NSSA研究而言,需要大量可用的数据集.
研究的目的:
- 为网络安全局势意识 (NSSA) 引入一个全新的,全面的数据集.
- 提供一个克服以前可用的网络安全数据集局限性的资源.
- 支持神经网络和其他机器学习模型的培训和评估.
主要方法:
- 在2017-2018年至2023-2024年期间,从监狱长系统收集了大约30亿个网络安全警报.
- 将原始警报处理成时间序列数据,适合机器学习应用.
- 采用了数据标签的"银色标准",警报由检测系统本身标记.
主要成果:
- 一个大量的,多部分数据集,包括来自数十亿网络安全警报的时间序列数据.
- 数据集跨越两个不同的时间段,捕捉不断变化的威胁景观.
- 数据来源于一个记录良好的和大量的来源,确保可用于研究的可用性.
结论:
- 这一数据集为网络安全研究界提供了宝贵的资源.
- 它可以开发更强大,更准确的NSSA系统,特别是那些使用深度学习的系统.
- 该倡议旨在通过提供可访问的大规模数据来推进网络安全领域.
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