摘要
提出了使用日志的孤立点分析方法,对日志数据进行预处理,确立合适的挖掘粒度,刻画出正常模式。改进的方法可对规模较大的数据集进行异常检测,在降低误报率的同时,大大提高检测率,并达到理想的时间效率;使系统定期分析用户日志,从中自动找到可疑的日志,及时预防或者处理非法操作的现象,提高检测系统的智能化、准确性和检测效率。
The use of outlier log analysis was proposed where the log data were preprocessed to establish the appropriate mining size and depict a normal mode. The improved method can be used for the large-scale anomalous detection of data sets, reducing the false alarm rate and greatly improving the detection rate to achieve the desired time efficiency. The system can analyze the users＇ logs regularly, find the suspect from the logs automatically, prevent and deal with the illegal operations in a timely manner. Therefore, it can improve the degree of intelligence, and the accuracy and efficiency of detection.
出处
《淮海工学院学报（自然科学版）》
CAS
2011年第1期24-28,共5页
Journal of Huaihai Institute of Technology:Natural Sciences Edition
关键词
日志
数据挖掘
孤立点
高维数据
log
data mining
outlier
high-dimensional data
