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

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’ profiles. Then, queries issued for each user are checked against the related user profile using a classifier to determine whether or not each query is malicious. The 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 evaluations. Normal user access behavior in relation to the database is modelled using the data set. The false negative (FN) and false positive (FP) rates are used to compare our proposed algorithm with other methods. The experimental results indicate that our proposed method results in very small FN and FP rates. Keywords Adaptive search memory; clustering; database management system (DBMS); intrusion detection system (IDS); quiplets; structured query language (SQL); tube search Cite This Article APA Style Alotaibi, Y. (2021). A New Database Intrusion Detection Approach Based on Hybrid Meta-Heuristics. Computers, Materials & Continua , 66 (2) , 1879–1895. https://doi.org/10.32604/cmc.2020.013739 Vancouver Style Alotaibi Y. A New Database Intrusion Detection Approach Based on Hybrid Meta-Heuristics. Comput Mater Contin. 2021;66(2):1879–1895. https://doi.org/10.32604/cmc.2020.013739 IEEE Style Y. Alotaibi, “A New Database Intrusion Detection Approach Based on Hybrid Meta-Heuristics,” Comput. Mater. Contin. , vol. 66, no. 2, pp. 1879–1895, 2021. https://doi.org/10.32604/cmc.2020.013739 BibTex EndNote RIS Citations 15 [click to view]