使用交叉序列对齐变压器检测网络安全行为的异常检测-用于高频交互模式的动态识别方法
Songming Han1, Dongmei Bin1, Ying Ling2
1Electric Power Research Institute of Guangxi Power Grid Co., Ltd., Nanning, Guangxi, China.
PloS one
|February 18, 2026
概括
这项研究引入了一种新模型,即交叉序列对齐变压器驱动的动态识别模型 (CSAT-DRM),以改善在高频网络中的网络安全异常检测. CSAT-DRM有效地处理网络流量和用户行为的时间异步,提高检测准确性和稳定性.
科学领域:
- 网络安全 网络安全
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 高频网络环境对异常检测具有挑战,原因是时间异步和流量和用户行为的数据冗余.
- 现有的模型很难在这些复杂的场景中有效地识别动态攻击模式.
研究的目的:
- 提出和验证一种新的深度学习模型,用于在高频互动网络中增强网络安全异常检测.
- 解决当前处理时间异步和信息冗余的方法的局限性.
主要方法:
- 开发了一个交叉序列对齐的变压器驱动的动态识别模型 (CSAT-DRM).
- 采用交叉序列对齐机制来关联多源时间序列数据而不会丢失时间信息.
- 整合了对相互作用敏感的残余结构和动态值,以改进异常歧视.
主要成果:
- CSAT-DRM实现了高性能指标:准确率为0.968,精度为0.957,回忆率为0.953,F1得分为0.955.
- 该模型显著超过了LSTM,CNN,变压器和CNN-BiLSTM等基线方法.
- 在检测爆发和持久异常方面表现出高稳定性的有效性.
结论:
- 显式建模时间异步,并在统一的时间尺度上执行协作建模,可以提高异常检测的准确性和稳定性.
- 在复杂的网络环境中,CSAT-DRM为实时威胁识别提供了可行和可泛化的技术途径.
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