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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This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract. (UD160066BD)
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Choi, SG., Cho, SB. (2018). Adaptive Database Intrusion Detection Using Evolutionary Reinforcement Learning. In: Pérez García, H., Alfonso-Cendón, J., Sánchez González, L., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding. SOCO ICEUTE CISIS 2017 2017 2017. Advances in Intelligent Systems and Computing, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-319-67180-2_53
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DOI: https://doi.org/10.1007/978-3-319-67180-2_53
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