通过协作变压器检测操作系统日志中的点和集体异常的统一框架
Mohammad Nasirzadeh1, Jafar Tahmoresnezhad2, Parviz Rashidi-Khazaee1
1Faculty of Information Technology and Computer Engineering, Urmia University of Technology, Band, Urmia, 57166-17165, West Azerbaijan, Iran.
Scientific reports
|December 28, 2025
概括
CoLog通过协同编码各种日志数据模式来增强日志异常检测. 这种先进的框架在识别系统威胁方面实现了卓越的准确性,提高了网络安全和运营效率.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 记录异常检测对于操作系统安全至关重要.
- 单模式方法忽略了日志数据的多样性,而多模式方法则难以处理多模式交互.
研究的目的:
- 提出 CoLog,这是一个新的框架,用于使用多种数据模式进行协作日志编码.
- 解决现有方法在处理日志数据异质性和相互作用方面的局限性.
主要方法:
- CoLog采用协作式变压器和多头感兴趣的注意力来学习模式间日志数据交互.
- 整合了一种模式适应层,以管理这些相互作用产生的数据异质性.
主要成果:
- 在日志异常检测方面,CoLog显著优于现有的最先进方法.
- 在七个基准数据集中实现了99.63%的平均精度,99.59%的回忆率和99.61%的F1得分.
- 在检测点和集体异常方面表现出有效性.
结论:
- CoLog提供了一个复杂而有效的统一框架,用于高级日志异常检测.
- 该框架通过细微的模式学习来增强网络安全,系统监控和运营效率.
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