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Learning Bayesian Network to Predict Group Emotion in Kindergarten by Evolutionary Computation Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Adaptive Database Intrusion Detection Using Evolutionary Reinforcement Learning

Authors : Seul-Gi Choi, Sung-Bae Cho

Published in: International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding

Publisher: Springer International Publishing

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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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previous chapter A New Simple Attack on a Wide Class of Cryptographic Sequence Generators
next chapter Learning Classifier Systems for Adaptive Learning of Intrusion Detection System
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Metadata
Title
Adaptive Database Intrusion Detection Using Evolutionary Reinforcement Learning
Authors
Seul-Gi Choi
Sung-Bae Cho
Copyright Year
2018
Book
International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding

Print ISBN: 978-3-319-67179-6

Electronic ISBN: 978-3-319-67180-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-67180-2_53

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