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
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.
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.
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