Genetic Algorithm-Based Deep Learning Ensemble for Detecting Database Intrusion via Insider Attack

• Bu, Seok-Jun ^[1] ; Cho, Sung-Bae ^[1]
  1. [1] Yonsei University

     Yonsei University

     Corea del Sur

     
• Localización: Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings / coord. por Hilde Pérez García, Lidia Sánchez González, Manuel Castejón Limas, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2019, ISBN 978-3-030-29858-6, págs. 145-156
• Idioma: inglés
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• Resumen

  • 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.