Abstract
A database Intrusion Detection System (IDS) based on Role-based Access Control (RBAC) mechanism that has capability of learning and adaptation learns SQL transaction patterns represented by roles to detect insider attacks. In this paper, we parameterize the rules for partitioning the entire query set into multiple areas with simple chromosomes and propose an ensemble of multiple deep learning models that can effectively model the tree structural characteristics of SQL transactions. Experimental results on a large synthetic query dataset verify that it quantitatively outperforms other ensemble methods and machine learning methods including deep learning models, in terms of 10-fold cross validation and chi-square validation.
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This research was supported by Korea Electric Power Corporation (Grant number: R18XA05).
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Bu, SJ., Cho, SB. (2019). Genetic Algorithm-Based Deep Learning Ensemble for Detecting Database Intrusion via Insider Attack. In: Pérez GarcÃa, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado RodrÃguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_13
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