首页> 外文会议>Chinese Conference on Trusted Computing and Information Security(CTCIS'06); 20061021-23; Baoding(CN) >Anomaly Detection System Based on Principal Component Analysis and Support Vector Machine
【24h】

Anomaly Detection System Based on Principal Component Analysis and Support Vector Machine

机译:基于主成分分析和支持向量机的异常检测系统

获取原文
获取原文并翻译 | 示例

摘要

This article presents an anomaly detection system based on principal component analysis (PCA) and support vector machine (SVM). The system first creates a profile defining a normal behavior by frequency-based scheme, and then compares the similarity of a current behavior with the created profile to decide whether the input instance is normal or anomaly. In order to avoid overfitting and reduce the computational burden, normal behavior principal features are extracted by the PCA method. SVM is used to distinguish normal or anomaly for user behavior after training procedure has been completed by learning. In the experiments for performance evaluation the system achieved a correct detection rate equal to 92.2% and a false detection rate equal to 2.8%.
机译:本文提出了一种基于主成分分析(PCA)和支持向量机(SVM)的异常检测系统。系统首先通过基于频率的方案创建一个定义正常行为的配置文件,然后将当前行为的相似性与创建的配置文件进行比较,以确定输入实例是正常还是异常。为了避免过度拟合并减少计算负担,通过PCA方法提取正常行为的主要特征。在通过学习完成训练过程之后,SVM用于区分用户行为的正常还是异常。在性能评估实验中,系统的正确检测率等于92.2%,错误检测率等于2.8%。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号