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Evolving neural network intrusion detection system for MCPS

Title
Evolving neural network intrusion detection system for MCPS
Authors
Mowla N.Doh I.Chae K.
Ewha Authors
채기준도인실
SCOPUS Author ID
채기준scopus; 도인실scopus
Issue Date
2018
Journal Title
International Conference on Advanced Communication Technology, ICACT
ISSN
1738-9445JCR Link
Citation
International Conference on Advanced Communication Technology, ICACT vol. 2018-February, pp. 1040 - 1045
Keywords
Intrusion Detection SystemMachine LearningMCPSNeural Networks
Publisher
Institute of Electrical and Electronics Engineers Inc.
Indexed
SCOPUS scopus
Document Type
Conference Paper
Abstract
Medical Cyber Physical Systems (MCPS) are some of the most promising next generation technologies so far. Like many other systems connected to a wider network such as internet, MCPS are also vulnerable to various forms of network attacks. For detecting such diverse forms of attack, we need smart and efficient mechanisms. Human intelligence is good enough to track such attacks but when it is a huge number of traffic it is no more a feasible process to detect them manually as it is time consuming and computationally intensive. Machine learning techniques embracing artificial intelligence are emerging as powerful tools to detect abnormalities in the network data. Supervised Neural Networks are some of the most efficient techniques to perform such classification. In this paper, we propose an evolving neural network technique that evolves based on classification, elimination and prioritization while focusing on time, space and accuracy to efficiently classify the four major types of network attack traffic found in an effectively pruned KDD dataset. We also show a leap of performance with hyper-parameter optimization which highly enhances the benefit of our proposed mechanism. Finally, the new performance gain is compared with a boosted Decision Tree. We believe our proposed mechanism can be adopted to new forms of attack categories and sub-categories. © 2018 Global IT Research Institute (GiRI).
DOI
10.23919/ICACT.2018.8323930
ISBN
9791188428007
Appears in Collections:
엘텍공과대학 > 컴퓨터공학과 > Journal papers
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