Evolutionary reinforcement learning for adaptively detecting database intrusions

Seul Gi Choi, Sung Bae Cho

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Relational database management system (RDBMS) is the most popular database system. It is important to maintain data security from information leakage and data corruption. RDBMS can be attacked by an outsider or an insider. It is difficult to detect an insider attack because its patterns are constantly changing and evolving. In this paper, we propose an adaptive database intrusion detection system that can be resistant to potential insider misuse using evolutionary reinforcement learning, which combines reinforcement learning and evolutionary learning. The model consists of two neural networks, an evaluation network and an action network. The action network detects the intrusion, and the evaluation network provides feedback to the detection of the action network. Evolutionary learning is effective for dynamic patterns and atypical patterns, and reinforcement learning enables online learning. Experimental results show that the performance for detecting abnormal queries improves as the proposed model learns the intrusion adaptively using Transaction Processing performance Council-E scenario-based virtual query data. The proposed method achieves the highest performance at 94.86%, and we demonstrate the usefulness of the proposed method by performing 5-fold cross-validation.

Original languageEnglish
Pages (from-to)449-460
Number of pages12
JournalLogic Journal of the IGPL
Volume28
Issue number4
DOIs
Publication statusPublished - 2020

Bibliographical note

Funding Information:
This work was partly supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University)) and Defense Acquisition Program Administration and Agency for Defense Development under the contract (UD190016ED).

Publisher Copyright:
© The Author(s) 2019. Published by Oxford University Press. All rights reserved.

All Science Journal Classification (ASJC) codes

  • Philosophy

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