Adaptive database intrusion detection using evolutionary reinforcement learning

Seul Gi Choi; Sung Bae Cho

doi:10.1007/978-3-319-67180-2_53

Adaptive database intrusion detection using evolutionary reinforcement learning

Seul Gi Choi, Sung Bae Cho

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

This paper proposes an adaptive database intrusion detection model that can be resistant to potential insider misuse with a limited number of data. The intrusion detection model can be adapted online using evolutionary reinforcement learning (ERL) which combines reinforcement learning and evolutionary learning. The model consists of two feedforward neural networks, a behavior network and an evaluation network. The behavior network detects the intrusion, and the evaluation network provides feedback to the detection of the behavior network. To find the optimal model, we encode the weights of the networks as an individual and produce populations of better individuals over generations. TPC-E scenario-based virtual query data were used for verification of the proposed model. Experimental results show that the detection performance improves as the proposed model learns the intrusion adaptively.

Original language	English
Title of host publication	International Joint Conference SOCO’17- CISIS’17-ICEUTE’17, Proceedings
Editors	Hilde Perez Garcia, Javier Alfonso-Cendon, Lidia Sanchez Gonzalez, Emilio Corchado, Hector Quintian
Publisher	Springer Verlag
Pages	547-556
Number of pages	10
ISBN (Print)	9783319671796
DOIs	https://doi.org/10.1007/978-3-319-67180-2_53
Publication status	Published - 2018
Event	International Joint Conference on 12th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2017, 10th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2017 and 8th International Conference on European Transnational Education, ICEUTE 2017 - Leon, Spain Duration: 2017 Sept 6 → 2017 Sept 8

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	649
ISSN (Print)	2194-5357

Other

Other	International Joint Conference on 12th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2017, 10th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2017 and 8th International Conference on European Transnational Education, ICEUTE 2017
Country/Territory	Spain
City	Leon
Period	17/9/6 → 17/9/8

Bibliographical note

Funding Information:
Acknowledgements.. This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract. (UD160066BD)

Publisher Copyright:
© 2018, Springer International Publishing AG.

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Computer Science(all)

Access to Document

10.1007/978-3-319-67180-2_53

Cite this

Choi, S. G., & Cho, S. B. (2018). Adaptive database intrusion detection using evolutionary reinforcement learning. In H. Perez Garcia, J. Alfonso-Cendon, L. Sanchez Gonzalez, E. Corchado, & H. Quintian (Eds.), International Joint Conference SOCO’17- CISIS’17-ICEUTE’17, Proceedings (pp. 547-556). (Advances in Intelligent Systems and Computing; Vol. 649). Springer Verlag. https://doi.org/10.1007/978-3-319-67180-2_53

Choi, Seul Gi ; Cho, Sung Bae. / Adaptive database intrusion detection using evolutionary reinforcement learning. International Joint Conference SOCO’17- CISIS’17-ICEUTE’17, Proceedings. editor / Hilde Perez Garcia ; Javier Alfonso-Cendon ; Lidia Sanchez Gonzalez ; Emilio Corchado ; Hector Quintian. Springer Verlag, 2018. pp. 547-556 (Advances in Intelligent Systems and Computing).

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abstract = "This paper proposes an adaptive database intrusion detection model that can be resistant to potential insider misuse with a limited number of data. The intrusion detection model can be adapted online using evolutionary reinforcement learning (ERL) which combines reinforcement learning and evolutionary learning. The model consists of two feedforward neural networks, a behavior network and an evaluation network. The behavior network detects the intrusion, and the evaluation network provides feedback to the detection of the behavior network. To find the optimal model, we encode the weights of the networks as an individual and produce populations of better individuals over generations. TPC-E scenario-based virtual query data were used for verification of the proposed model. Experimental results show that the detection performance improves as the proposed model learns the intrusion adaptively.",

author = "Choi, {Seul Gi} and Cho, {Sung Bae}",

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Choi, SG & Cho, SB 2018, Adaptive database intrusion detection using evolutionary reinforcement learning. in H Perez Garcia, J Alfonso-Cendon, L Sanchez Gonzalez, E Corchado & H Quintian (eds), International Joint Conference SOCO’17- CISIS’17-ICEUTE’17, Proceedings. Advances in Intelligent Systems and Computing, vol. 649, Springer Verlag, pp. 547-556, International Joint Conference on 12th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2017, 10th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2017 and 8th International Conference on European Transnational Education, ICEUTE 2017, Leon, Spain, 17/9/6. https://doi.org/10.1007/978-3-319-67180-2_53

Adaptive database intrusion detection using evolutionary reinforcement learning. / Choi, Seul Gi; Cho, Sung Bae.
International Joint Conference SOCO’17- CISIS’17-ICEUTE’17, Proceedings. ed. / Hilde Perez Garcia; Javier Alfonso-Cendon; Lidia Sanchez Gonzalez; Emilio Corchado; Hector Quintian. Springer Verlag, 2018. p. 547-556 (Advances in Intelligent Systems and Computing; Vol. 649).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Adaptive database intrusion detection using evolutionary reinforcement learning

AU - Choi, Seul Gi

AU - Cho, Sung Bae

N1 - Funding Information: Acknowledgements.. This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract. (UD160066BD) Publisher Copyright: © 2018, Springer International Publishing AG.

PY - 2018

Y1 - 2018

N2 - This paper proposes an adaptive database intrusion detection model that can be resistant to potential insider misuse with a limited number of data. The intrusion detection model can be adapted online using evolutionary reinforcement learning (ERL) which combines reinforcement learning and evolutionary learning. The model consists of two feedforward neural networks, a behavior network and an evaluation network. The behavior network detects the intrusion, and the evaluation network provides feedback to the detection of the behavior network. To find the optimal model, we encode the weights of the networks as an individual and produce populations of better individuals over generations. TPC-E scenario-based virtual query data were used for verification of the proposed model. Experimental results show that the detection performance improves as the proposed model learns the intrusion adaptively.

AB - This paper proposes an adaptive database intrusion detection model that can be resistant to potential insider misuse with a limited number of data. The intrusion detection model can be adapted online using evolutionary reinforcement learning (ERL) which combines reinforcement learning and evolutionary learning. The model consists of two feedforward neural networks, a behavior network and an evaluation network. The behavior network detects the intrusion, and the evaluation network provides feedback to the detection of the behavior network. To find the optimal model, we encode the weights of the networks as an individual and produce populations of better individuals over generations. TPC-E scenario-based virtual query data were used for verification of the proposed model. Experimental results show that the detection performance improves as the proposed model learns the intrusion adaptively.

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T3 - Advances in Intelligent Systems and Computing

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BT - International Joint Conference SOCO’17- CISIS’17-ICEUTE’17, Proceedings

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Y2 - 6 September 2017 through 8 September 2017

ER -

Choi SG, Cho SB. Adaptive database intrusion detection using evolutionary reinforcement learning. In Perez Garcia H, Alfonso-Cendon J, Sanchez Gonzalez L, Corchado E, Quintian H, editors, International Joint Conference SOCO’17- CISIS’17-ICEUTE’17, Proceedings. Springer Verlag. 2018. p. 547-556. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-67180-2_53

Adaptive database intrusion detection using evolutionary reinforcement learning

Abstract

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Bibliographical note

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