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PACKETCLIP:网络流量的多模式嵌入和网络安全推理语言

Ryozo Masukawa¹, Sanggeon Yun¹, Sungheon Jeong¹

¹Department of Computer Science, University of California, Irvine, Irvine, CA, United States.

Frontiers in artificial intelligence

|August 12, 2025

概括

PACKETCLIP通过使用自然语言和数据包分析加密流量来增强网络安全. 这种新的框架改善了异常检测和入侵识别,提供了可扩展和可解释的解决方案.

科学领域:

网络安全网络安全
机器学习机器学习
网络安全网络安全

背景情况:

加密流量对传统的网络安全方法构成重大挑战.
有效的流量分类对于检测网络异常和入侵至关重要.

研究的目的:

引入PACKETCLIP,一个用于加密流量分类的多模式框架.
将自然语言语义与数据包集成为增强异常检测.
为网络安全挑战提供可扩展,可解释和高效的解决方案.

主要方法:

使用结合数据包和自然语言语义的多模式框架.
使用对比的预训练和层次的图形神经网络 (GNN) 推理.
将文本描述与数据包行为对齐以实现语义集成.

主要成果:

实现了95%的曲线下的平均面积 (AUC),比现有方法提高了11.6%.
在入侵检测培训参数中显示了92%的减少.
在加密网络流中展示了强大的异常检测.

结论:

在各种安全场景中,PACKETCLIP提供了增强的解释性,可扩展性和实际适用性.
该框架有效地将先进的机器学习与实际的网络安全需求联系起来.
PACKETCLIP为在资源有限的环境中实时检测异常提供了基础.

关键词:

与之相反的,这是一个预先培训.图表神经网络的神经网络机器学习是机器学习.多式多样化的多式模式推理推理推理

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