从加密视频会议流量中估计QOE.
Michael Sidorov1, Raz Birman1, Ofer Hadar1
1School of Electrical and Computer Engineering, Ben Gurion University of the Negev, Be'er Sheba 8410501, Israel.
Sensors (Basel, Switzerland)
|February 26, 2025
概括
这项研究预测了使用加密流量进行视频会议的体验质量 (QoE). 机器学习模型准确地估计了关键指标,如每秒 (FPS) 和分辨率 (R),改进了互联网安全分析.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 信息安全 信息安全
背景情况:
- 互联网安全依赖于流量加密,这阻碍了对视频交付优化和体验质量 (QoE) 估计等应用程序的分析.
- 现有的QoE预测模型主要集中在清晰文本流量上,并且经常提供低分辨率的分类预测,使视频会议 (VC) 领域未得到充分探索.
研究的目的:
- 解决视频会议 (VC) 应用程序中加密流量的 QoE 评估中的挑战.
- 开发准确,连续的风险投资者QOE预测,超越广泛的类别.
- 分析加密的流量数据以改善QOE估计.
主要方法:
- 对包含Zoom会话的大数据集的分析.
- 培训和评估五种经典机器学习 (ML) 模型.
- 开发和培训两个定制深度神经网络 (DNN).
- 预测三个关键的QOE指标:每秒 (FPS),分辨率 (R) 和自然性图像质量评估器 (NIQE).
- 使用十倍交叉验证技术进行可靠的模型评估.
主要成果:
- 在FPS预测中实现了8.27%的平均错误率.
- 实现了分辨率 (R) 预测的7.56%的平均错误率.
- 对于自然性图像质量评估器 (NIQE) 预测的平均错误率为2.08%.
结论:
- 开发的模型表明,在VC应用程序中对加密流量进行QOE评估方面取得了重大进展.
- 即使使用加密的视频会议数据,也可以实现精确,连续的QOE预测.
- 这项研究为增强网络管理和视频会议服务用户体验提供了基础.
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