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GeNIS:用于网络入侵检测和分类的模块化数据集.

Miguel Silva1, Daniela Pinto1, João Vitorino1

  • 1Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), School of Engineering, Polytechnic of Porto (ISEP/IPP), 4249-015 Porto, Portugal.

Data in brief
|April 14, 2025
PubMed
概括

一个新的数据集,GeNIS,为中小企业提供现实的网络攻击和正常网络流量数据. 这个资源有助于开发更好的人工智能驱动的入侵检测系统.

关键词:
异常检测检测异常检测攻击分类攻击分类.网络安全 网络安全数据集数据集数据集机器学习 机器学习网络流量 网络流量封装包捕获 (packet capture) 是一种可以捕获数据包的方法.

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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:

  • 高质量的数据对于人工智能驱动的网络攻击检测至关重要.
  • 缺乏标记数据集阻碍了中小企业开发入侵检测系统 (IDS).
  • 需要现实的网络流量数据来将IDS定制为特定的组织需求.

研究的目的:

  • 为了引入GECAD网络入侵情景 (GeNIS) 数据集.
  • 为培训和评估用于检测网络攻击的AI模型提供一个基准数据集.
  • 支持改善中小企业的入侵检测系统.

主要方法:

  • 在Airbus CyberRange平台上记录了现实的正常和攻击网络流量.
  • 由原始数据包捕获 (PCAPNG) 生成的标记网络流.
  • 计算统计特征并创建各种流程间隔的过的CSV文件.

主要成果:

  • 吉尼斯数据集包括超过3700万个数据包和2800万个数据流.
  • 功能代表不同的流量模式:攻击者,普通用户,管理员和后台流量.
  • 数据集预处理用于机器学习和深度学习模型培训.

结论:

  • 对于中小企业来说,GeNIS解决了标记的网络攻击数据集的稀缺问题.
  • 该数据集使深入分析和开发强大的入侵检测模型成为可能.
  • 它有助于创建更有效和更具适应性的网络安全解决方案.