对比式基于学习的异常检测用于实际的企业环境.
Gi-Taek An1,2, Jung-Min Park1, Kyung-Soon Lee2
1Korea Food Research Institute, Wanju-gun 55365, Republic of Korea.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
这项研究引入了一种新的深度学习方法,用于检测企业信息系统中的异常. 这种方法在小型数据集中表现出色,在异常检测中达到99.47%的真正阳性率.
科学领域:
- 计算机科学 计算机科学
- 信息系统信息系统信息系统
- 机器学习 机器学习
背景情况:
- 信息系统对于业务运营至关重要,包括人事,预算和财务管理.
- 系统异常可能导致完全的操作,需要强大的检测方法.
- 从实时的企业系统中收集和标记异常数据,由于稳定性约束和数据不平衡,存在重大挑战.
研究的目的:
- 提出一种深度学习方法,用于在企业信息系统中检测异常.
- 在现实世界操作环境中解决小型和不平衡数据集的挑战.
- 开发一种适用于难以收集异常数据的环境的方法.
主要方法:
- 利用对比式学习与通过负采样进行数据增强.
- 专注于从实际的企业操作系统创建和标记数据集.
- 将拟议的方法与传统的深度学习模型 (如卷积神经网络 (CNN) 和长短期记忆 (LSTM)) 进行比较.
主要成果:
- 拟议的方法实现了 99.47% 的真实阳性率 (TPR).
- CNN和LSTM模型实现了较低的TPR,分别为98.8%和98.67%.
- 在异常检测方面表现出卓越的有效性,特别是对于小型数据集.
结论:
- 提出的对比学习方法对于企业信息系统的异常检测非常有效.
- 这种技术在处理小或不平衡的数据集时特别有利.
- 这种方法为维护关键业务系统的稳定性和完整性提供了可行的解决方案.
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