A New Database Intrusion Detection Approach Based on Hybrid Meta-Heuristics

作     者：Youseef Alotaibi 

作者机构：Department of Computer ScienceCollege of Computer and Information SystemsUmm Al Qura UniversityMakkah21421Saudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第66卷第2期

页      面：1879-1895页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Adaptive search memory clustering database management system(DBMS) intrusion detection system(IDS) quiplets structured query language(SQL) tube search 

摘      要：A new secured database management system architecture using intrusion detection systems(IDS)is proposed in this paper for organizations with no previous role mapping for users.A simple representation of Structured Query Language queries is proposed to easily permit the use of the worked clustering algorithm.A new clustering algorithm that uses a tube search with adaptive memory is applied to database log files to create users’***,queries issued for each user are checked against the related user profile using a classifier to determine whether or not each query is *** IDS will stop query execution or report the threat to the responsible person if the query is malicious.A simple classifier based on the Euclidean distance is used and the issued query is transformed to the proposed simple representation using a classifier,where the Euclidean distance between the centers and the profile’s issued query is calculated.A synthetic data set is used for our experimental *** user access behavior in relation to the database is modelled using the data *** false negative(FN)and false positive(FP)rates are used to compare our proposed algorithm with other *** experimental results indicate that our proposed method results in very small FN and FP rates.