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Performance Enhancement of Deep Neural Network Using Feature Selection and Preprocessing for Intrusion Detection
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Publication Year
2019-03-18
Journal
1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019, pp.415-417
Keyword
Layer configurationMachine learningNetwork securityNSL-KDDPearson correlation coefficient
Mesh Keyword
Data-mining toolsIntrusion Detection SystemsLayer configurationNetwork attackNSL-KDDPearson correlation coefficientsPerformance enhancementsReal time performance
All Science Classification Codes (ASJC)
Electrical and Electronic EngineeringComputer Science ApplicationsArtificial Intelligence
Abstract
Machine learning enables intrusion detection systems to detect network attacks adaptively and intelligently. Recently deep neural network has been investigated as such a solution owing to its high accuracy but it has limitation in real-Time performance. To enhance the learning time, in this paper, we propose to use feature selection and layer configuration. We use the NSL-KDD data set, which is a refined version of the KDD CUP 99 data set and analyzed the associations between features using WEKA, a data mining tool. Our experimental results confirm that proper feature selection and layer configuration can reduce learning time while maintaining high average accuracy.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36433
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85063878578&origin=inward
DOI
https://doi.org/10.1109/icaiic.2019.8668995
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8665865
Type
Conference
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Choi, Youngjune Image
Choi, Youngjune최영준
Department of Software and Computer Engineering
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