Adaptive database intrusion detection using evolutionary reinforcement learning

Seul Gi Choi, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Publication series

NameAdvances in Intelligent Systems and Computing
Volume649
ISSN (Print)2194-5357

Other

OtherInternational 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
CountrySpain
CityLeon
Period17/9/617/9/8

Fingerprint

Reinforcement learning
Intrusion detection
Feedforward neural networks
Feedback

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Choi, S. G., & Cho, S. B. (2018). Adaptive database intrusion detection using evolutionary reinforcement learning. In 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. 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.",
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Choi, SG & Cho, SB 2018, Adaptive database intrusion detection using evolutionary reinforcement learning. in 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. Springer Verlag, 2018. p. 547-556 (Advances in Intelligent Systems and Computing; Vol. 649).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Choi SG, Cho SB. Adaptive database intrusion detection using evolutionary reinforcement learning. In International Joint Conference SOCO’17- CISIS’17-ICEUTE’17, Proceedings. Springer Verlag. 2018. p. 547-556. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-319-67180-2_53