Database management systems based on role-based access control are widely used for information storage and analysis, but they are reportedly vulnerable to insider attacks. From the point of adaptive system, it is possible to perform classification on user queries accessing the database to determine insider attacks when they differ from the predicted values. In order to cope with high similarity of user queries, this paper proposes a deep metric neural network with hierarchical structure that extracts the salient features appropriately and learns the quantitative scale of similarity directly. The proposed model trained with 11,000 queries for 11 roles from the benchmark dataset of TPC-E produces the classification accuracy of 94.17%, which is the highest compared to the previous studies. The quantitative performance is evaluated by 10-fold cross-validation, the feature space embedded in the neural network is visualized by t-SNE, and the qualitative analysis is conducted by clustering the compression vectors among classes.
|Title of host publication||13th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2020|
|Editors||Álvaro Herrero, Carlos Cambra, Daniel Urda, Javier Sedano, Héctor Quintián, Emilio Corchado|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||10|
|Publication status||Published - 2021|
|Event||13th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2020 - Burgos, Spain|
Duration: 2020 Sep 16 → 2020 Sep 18
|Name||Advances in Intelligent Systems and Computing|
|Conference||13th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2020|
|Period||20/9/16 → 20/9/18|
Bibliographical noteFunding Information:
This research was supported by Samsung Electronics Co., Ltd.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Computer Science(all)