Network intrusion detection based on deep belief network and PSO-SVM

Bo Wang; Donggyun Kim; Kaoru Hirota; Yaping Dai

Network intrusion detection based on deep belief network and PSO-SVM

Bo Wang^*, Donggyun Kim, Kaoru Hirota, Yaping Dai

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

Research output: Contribution to conference › Paper › peer-review

Abstract

A hybrid algorithm for coupling PSO-SVM with Deep Belief Network (DBN) is applied to improve the recognition accuracy of intrusion detection. The hybrid algorithm has two parts, DBN is utilized to reduce the dimensionality of the feature sets. This is followed by a PSO-SVM to classify the intrusion into five outcome. The DBN reduces the dimension for the latency of data. And the PSO-SVM avoids a local optima. Simulation experiments using NSL-KDD dataset show that the proposed algorithm has higher detecting probability 93.83%, improve 1.66% compared with DBN-SVM.

Original language	English
Publication status	Published - 2018
Event	8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018 - Tengzhou, Shandong, China Duration: 2 Nov 2018 → 6 Nov 2018

Conference

Conference	8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018
Country/Territory	China
City	Tengzhou, Shandong
Period	2/11/18 → 6/11/18

Keywords

Deep belief Network
Intrusion Detection
NSL-KDD
Particle Swarm Optimization with Support Vector Machine

Cite this

Wang, Bo ; Kim, Donggyun ; Hirota, Kaoru et al. / Network intrusion detection based on deep belief network and PSO-SVM. Paper presented at 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018, Tengzhou, Shandong, China.

@conference{047380c97d2345059b69a2cd2bc6063d,

title = "Network intrusion detection based on deep belief network and PSO-SVM",

abstract = "A hybrid algorithm for coupling PSO-SVM with Deep Belief Network (DBN) is applied to improve the recognition accuracy of intrusion detection. The hybrid algorithm has two parts, DBN is utilized to reduce the dimensionality of the feature sets. This is followed by a PSO-SVM to classify the intrusion into five outcome. The DBN reduces the dimension for the latency of data. And the PSO-SVM avoids a local optima. Simulation experiments using NSL-KDD dataset show that the proposed algorithm has higher detecting probability 93.83%, improve 1.66% compared with DBN-SVM.",

keywords = "Deep belief Network, Intrusion Detection, NSL-KDD, Particle Swarm Optimization with Support Vector Machine",

author = "Bo Wang and Donggyun Kim and Kaoru Hirota and Yaping Dai",

note = "Publisher Copyright: {\textcopyright} ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Application.; 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018 ; Conference date: 02-11-2018 Through 06-11-2018",

year = "2018",

language = "English",

}

Wang, B, Kim, D, Hirota, K & Dai, Y 2018, 'Network intrusion detection based on deep belief network and PSO-SVM', Paper presented at 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018, Tengzhou, Shandong, China, 2/11/18 - 6/11/18.

Network intrusion detection based on deep belief network and PSO-SVM. / Wang, Bo; Kim, Donggyun; Hirota, Kaoru et al.
2018. Paper presented at 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018, Tengzhou, Shandong, China.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Network intrusion detection based on deep belief network and PSO-SVM

AU - Wang, Bo

AU - Kim, Donggyun

AU - Hirota, Kaoru

AU - Dai, Yaping

N1 - Publisher Copyright: © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Application.

PY - 2018

Y1 - 2018

N2 - A hybrid algorithm for coupling PSO-SVM with Deep Belief Network (DBN) is applied to improve the recognition accuracy of intrusion detection. The hybrid algorithm has two parts, DBN is utilized to reduce the dimensionality of the feature sets. This is followed by a PSO-SVM to classify the intrusion into five outcome. The DBN reduces the dimension for the latency of data. And the PSO-SVM avoids a local optima. Simulation experiments using NSL-KDD dataset show that the proposed algorithm has higher detecting probability 93.83%, improve 1.66% compared with DBN-SVM.

AB - A hybrid algorithm for coupling PSO-SVM with Deep Belief Network (DBN) is applied to improve the recognition accuracy of intrusion detection. The hybrid algorithm has two parts, DBN is utilized to reduce the dimensionality of the feature sets. This is followed by a PSO-SVM to classify the intrusion into five outcome. The DBN reduces the dimension for the latency of data. And the PSO-SVM avoids a local optima. Simulation experiments using NSL-KDD dataset show that the proposed algorithm has higher detecting probability 93.83%, improve 1.66% compared with DBN-SVM.

KW - Deep belief Network

KW - Intrusion Detection

KW - NSL-KDD

KW - Particle Swarm Optimization with Support Vector Machine

UR - http://www.scopus.com/inward/record.url?scp=85057969326&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:85057969326

T2 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018

Y2 - 2 November 2018 through 6 November 2018

ER -

Network intrusion detection based on deep belief network and PSO-SVM

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Keywords

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