一个基于可拆卸卷积GCN-LSTM的新型加密流量检测模型
Xiaogang Yuan1, Jianxin Wan2, Dezhi An2
1School of Cyber Security, Gansu University of Political Science and Law, Lanzhou, Gansu, China. xiaogang061218@163.com.
Scientific reports
|July 30, 2025
概括
本研究介绍了可拆卸卷积GCN-LSTM (DC-GL) 模型,用于检测恶意加密流量. DC-GL模型有效地捕捉了结构和时间特征,在准确性和稳定性方面超过了传统方法.
科学领域:
- 网络安全 网络安全
- 网络安全 网络安全
- 机器学习 机器学习
背景情况:
- 由于功能提取有限,传统方法与加密恶意流量作斗争.
- 网络加密的采用需要先进的检测技术.
研究的目的:
- 为恶意加密流量检测提出一个有效的模型.
- 增强加密流中的结构和行为特征的识别.
主要方法:
- 开发了一个可拆卸的卷积GCN-LSTM (DC-GL) 模型.
- 将协议层和统计流量特征集成到图形结构数据中.
- 用于结构依赖的使用图形卷积网络 (GCN) 和用于时间动态的长短期记忆 (LSTM).
- 包含可拆卸的卷积和注意力机制,以提高效率和功能增强.
主要成果:
- DC-GL模型在主流方法中表现出优越的性能.
- 在检测恶意加密流量方面实现了更高的准确性,回忆力和稳定性.
- 与现有方法相比,表现出更快的趋同.
结论:
- DC-GL模型为恶意加密流量检测提供了一个有希望和有效的解决方案.
- 集成GCN,LSTM,可拆卸卷积和注意力机制可以提高检测能力.
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