摘要
提出了一种基于聚类和主成分分析的异常检测方法,该方法利用聚类分析将训练数据划分为不同的子集,从而得到正常模式在特征空间中的分布,然后利用主成分分析来提取各行为子集的特征轮廓,最后利用各子集的PCA变换矩阵进行检测。实验结果证明了基于主成分分析的异常检测方法的有效性。
An anomaly detection method based on clustering and principal component analysis is proposed.The method partitions the train data set into several sub-sets to get the distribution of the normal pattern in feature space.Then it extracts the feature contour of each sub-set.Finally it detects behavior records by the PCA matrix of each sub-set.The results of the experiment show that the anomaly detection method based on principal component analysis is effective.
出处
《计算机工程与应用》
CSCD
北大核心
2006年第21期21-24,共4页
Computer Engineering and Applications
基金
公安部重点支持项目(编号:200342-823-01)
关键词
入侵检测
异常检测
聚类
主成分分析
intrusion detection,anomaly detection,clustering,principal component analysis
