A convolutional neural-based learning classifier system for detecting database intrusion via insider attack

Abstract Role-based access control (RBAC) in databases provides a valuable level of abstraction to promote security administration at the business enterprise level. With the capacity for adaptation and learning, machine learning algorithms are suitable for modeling normal data access patterns based on large amounts of data and presenting robust statistical models that are not sensitive to user changes. We propose a convolutional neural-based learning classifier system (CN-LCS) that models the role of queries by combining conventional learning classifier system (LCS) with convolutional neural network (CNN) for a database intrusion detection system based on the RBAC mechanism. The combination of modified Pittsburgh-style LCSs for the optimization of feature selection rules and one-dimensional CNNs for modeling and classification in place of traditional rule generation outperforms other machine learning classifiers on a synthetic query dataset. In order to quantitatively compare the inclusion of rule generation and modeling processes in the CN-LCS, we have conducted 10-fold cross-validation tests and analysis through a paired sampled t-test.