关键词: 数据库安全 异常检测 聚类分析 关联规则挖掘

Abstract:

Based on clustering analysis and associate rule mining, an efficient anomalous detection approach is presented, and a database anomalous detection arc  hitecture is designed. The critical technology of the architecture is also studied in this paper. KMApriori, the database anomaly detection algorithm wh   ich is presented in this paper, can detect the anomalous op- erations of users, and is superior to the database anomaly detection algorithm Apriori which is based on associate rule mining. The more database records, the more distinctness of the superiority. Furthermore, the detection rate of KMApriori is also higher than Apriori.

Key words: database security;anomaly detection, clustering analysis, associate rule mining