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在生产无线网格社区网络中检测异常的数据集
Llorenç Cerdà-Alabern1, Gabriel Iuhasz2
1Departament d'Arquitectura de Computadors (DAC), Universitat Politécnica de Catalunya - BarcelonaTech (UPC), Campus Nord, Edif. D6, C. Jordi Girona, 1-3, Barcelona 08034, Spain.
Data in brief
|July 14, 2023
概括
本研究引入了生产无线社区网络 (WCN) 的新数据集,以应对异常检测方面的挑战. 这些数据可以研究机器学习,用于在动态WCN环境中检测故障.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 数据科学数据科学数据科学
背景情况:
- 无线社区网络 (WCN) 正在全球范围内发展,利用低成本的WiFi设备为用户构建的基础设施.
- WCNs的动态性质,频繁的节点变化和异质链接,对异常检测提出了重大挑战.
- 现有的研究缺乏全面的数据集,用于研究在这些复杂的WCN环境中检测故障.
研究的目的:
- 提供从生产 WCN.收集的新数据集.
- 为了促进对无监督机器学习算法的调查,用于WCN中的故障检测.
- 为在动态和异质网络条件下评估异常检测技术提供一个基准.
主要方法:
- 从中央服务器收集数据,在生产 WCN 中汇总来自 63 个网格节点的信息.
- 收集了各种功能,包括流量,CPU,内存和网络拓 (邻近矩阵,路由表,指标).
- 包括一个已知的,未引起的网关故障事件在数据集内用于故障检测分析.
主要成果:
- 现实世界WCN的综合数据集现在已经可用了.
- 数据集包含有关网络行为,节点性能和拓学的丰富信息.
- 包括一个特定的故障事件允许有针对性的算法评估.
结论:
- 这一数据集是研究生产WCN中故障检测的首个数据集.
- 它为推进WCN异常检测和机器学习应用研究提供了宝贵的资源.
- 能够对为动态和易发生故障的网络基础设施设计的算法进行实证评估.
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