Optimizing CNN-LSTM neural networks with PSO for anomalous query access control

Abstract

Database security focuses on protecting most organization’s virtual data storage unit and confidential information from malicious threats and external attacks. To keep out data secure, we need to use a role-based access control (RBAC) approach to accurately differentiate access permissions, but SQL queries written by an authorized user have very similar characteristics and are difficult to distinguish. In this paper, we propose a method of optimizing CNN-LSTM neural networks with particle swarm optimization (PSO) to classify the roles in RBAC system. Convolutional neural network (CNN) can extract parsed SQL queries into smaller details and features through an analysis mechanism. Long short-term memory (LSTM) is also suitable for modeling the temporal information of SQL queries to recognize the context of user authorities. PSO repeatedly searches and optimizes the complex hyperparameter space of the CNN-LSTM. Our PSO-based CNN-LSTM neural networks outperform other deep learning and machine learning models in the TPC-E benchmark SQL query statement. Finally, experiments and analysis show the usefulness of PSO and identify the important SQL query features that affect user role classification.