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.

中文翻译：

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进化强化学习用于自适应检测数据库入侵

关系数据库管理系统（RDBMS）是最流行的数据库系统。保持数据安全以防止信息泄漏和数据损坏非常重要。RDBMS可能受到局外人或局内人的攻击。难以检测到内部攻击，因为内部攻击的模式不断变化和发展。在本文中，我们提出了一种自适应数据库入侵检测系统，该系统可以使用结合了强化学习和进化学习的进化强化学习来抵御潜在的内部滥用。该模型由两个神经网络组成，一个评估网络和一个动作网络。动作网络检测到入侵，评估网络向动作网络的检测提供反馈。进化学习对于动态模式和非典型模式非常有效，强化学习使在线学习成为可能。实验结果表明，随着基于事务处理性能Council-E场景的虚拟查询数据自适应地学习入侵，该模型提高了异常查询的检测性能。所提出的方法达到了94.86％的最高性能，并且我们通过执行5倍交叉验证证明了所提出方法的有效性。