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.
|Title of host publication||Hybrid Artificial Intelligent Systems - 14th International Conference, HAIS 2019, Proceedings|
|Editors||Hilde Pérez García, Lidia Sánchez González, Manuel Castejón Limas, Héctor Quintián Pardo, Emilio Corchado Rodríguez|
|Number of pages||12|
|Publication status||Published - 2019|
|Event||14th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2019 - León, Spain|
Duration: 2019 Sep 4 → 2019 Sep 6
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||14th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2019|
|Period||19/9/4 → 19/9/6|
Bibliographical noteFunding Information:
This research was supported by Korea Electric Power Corporation. (Grant number: R18XA05).
© 2019, Springer Nature Switzerland AG.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)