ITC-Net-blend-60:一个全面的数据集,用于在不同的环境中进行可靠的网络流量分类
Marziyeh Bayat1, Javad Garshasbi1, Mozhgan Mehdizadeh1
1Information Theory and Coding (ITC) Laboratory, University of Tehran, Tehran, Iran.
BMC research notes
|June 15, 2024
概括
本研究引入了多样化的移动网络流量数据集,以改善加密流量的移动应用程序识别. 该数据集增强了网络流量分类器的稳定性和现实世界的应用性.
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:
- 在加密网络流量中识别移动应用程序对于网络管理,安全和数字营销至关重要.
- 现有的数据集是有限的,因为它们来自单一的网络环境,阻碍模型评估现实世界的稳定性.
- 开发适应动态设置的可适应网络流量分类器仍然是一个重大挑战.
研究的目的:
- 通过创建更全面和多样化的移动网络流量数据集来解决当前数据集的局限性.
- 促进开发和评估可靠的移动应用程序识别模型.
- 支持网络安全和流量分析方面的研究.
主要方法:
- 从五个不同的网络场景中收集了60个流行的Android应用程序的流量数据.
- 随着互联网服务提供商 (ISP),地理位置,设备,应用程序版本和用户的变化而改变各种场景.
- 通过物理设备上的真实人际交互捕获的流量,不包括后台流量和不需要root访问.
主要成果:
- 该数据集包含超过4800万个数据包,45万个双向流,以及36GB的数据.
- 在各种网络条件下,来自60个应用程序的流量产生.
- 数据收集方法确保了实际可用性,并避免了特权访问.
结论:
- 新创建的数据集为培训和验证移动应用程序识别模型提供了宝贵的资源.
- 它在网络条件中的多样性增强了对分类器稳定性和现实世界的性能的评估.
- 这项工作通过改进的流量分析,有助于推进网络安全和智能网络管理.
相关概念视频
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