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此摘要是机器生成的。

本研究介绍了LADSVM,这是一种用于检测虚拟机日志中的异常的新方法. LADSVM有效地识别安全风险和系统故障,即使有噪音数据,提高检测准确度.

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科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 机器学习 机器学习

背景情况:

  • 虚拟机日志产生大量数据,可能包含显示安全风险或系统故障的异常.
  • 识别这些异常对于维护系统完整性至关重要,但现实世界日志数据经常受到噪音和收集挑战的影响.
  • 由于日志复杂性和固有的噪音,现有的方法可能会在准确性和效率方面扎.

研究的目的:

  • 为虚拟机日志提出和评估一个强大的无监督异常检测方法.
  • 为了提高在存在杂和复杂的日志数据的情况下检测异常的准确性和有效性.
  • 解决日志解析和特征提取的挑战,以改善异常识别.

主要方法:

  • 日志解析用于预处理原始日志数据.
  • 一种混合特征提取技术,结合长短期内存 (LSTM) 和自动编码解码器,以减少维度和消除噪音.
  • 支持矢量机 (SVM) 用于分类提取的特征和检测异常.

主要成果:

  • 拟议的LADSVM方法在检测虚拟机日志中的异常方面表现优于传统方法.
  • 这种方法有效地学习了相关的特征,而没有先前的知识,表现出对噪声的增强强性.
  • 实验结果证实了该方法在处理顺序模式和杂日志数据方面的能力.

结论:

  • LADSVM提供了一种强大而准确的解决方案,用于在虚拟机日志中无监督检测异常,特别是那些具有序列模式和噪声的日志.
  • 该方法的噪声稳定性和在没有先前知识的情况下学习特征的能力代表了重大进步.
  • 基于日志数据特征的检测方法的仔细选择对于最佳性能至关重要.