Abstract
Relational databases contain information that must be protected such as personal information, the problem of intrusion detection of relational database is considered important. Also, the pattern of attacks evolves and it is difficult to grasp by rule-based method or general machine learning, so adaptive learning is needed. Learning classifier systems are system that combines supervised learning, reinforcement learning and evolutionary computation. It creates and updates classifiers according to data input. Learning classifier systems can learn adaptive because they generate and evaluate classifiers in real time. In this paper, we apply accuracy based learning classifier systems to relational database and confirm that adaptive learning is possible. Also, we confirmed their practical usability that they close to the best accuracy, though were not the best.
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Acknowledgements
This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract. (UD160066BD).
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Lee, C.S., Cho, S.B. (2018). Learning Classifier Systems for Adaptive Learning of Intrusion Detection System. 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_54
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DOI: https://doi.org/10.1007/978-3-319-67180-2_54
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