research-article

An Intrusion Detection Model Based on Deep Belief Network

Authors:

Feng Qu,

Jitao Zhang,

Zetian Shao,

Shuzhuang QiAuthors Info & Claims

ICNCC '17: Proceedings of the 2017 VI International Conference on Network, Communication and Computing

Pages 97 - 101

https://doi.org/10.1145/3171592.3171598

Published: 08 December 2017 Publication History

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Abstract

The method of network intrusion detection based on deep learning is the hotspot of current research. This paper presents an intrusion detection model based on deep belief network and analyzes the NSL-KDD dataset. The experimental results show that the algorithm improves the detection of abnormal behavior pattern data and satisfies the requirement of high efficiency and reliability of intrusion detection system.

References

[1]

Anderson J P. Computer security threat monitoring and surveillance{R}. Technical report, James P. Anderson Company, Fort Washington, Pennsylvania, 1980.

Google Scholar

[2]

Tsai C F,Hsu Y F,Lin C Y,et al.Intrusion detection by machine learning:A review{J}. Expert System with Applications, 2009,36(10):11994--12000.

Digital Library

Google Scholar

[3]

Shah B,Trivedi B H.Artificial neural network based intrusion detection systems:a survey{J}. International Journal of Computer Applications.2012,39(6):13--18.

Google Scholar

[4]

Rao X, Dong C X,Yang S Q. An Intrusion Detection System Based on Support Vector Machine{J}. Journal of Software,2003.14(4):798--803.

Google Scholar

[5]

MS Pervez,DM Farid. Feature selection and intrusion classification in NSL-KDD cup 99 dataset employing SVMs{J}. International Conference on Software,2015:1--6

Google Scholar

[6]

S Lakhina,S Joseph,B Verma. Feature Reduction using Principal Component Analysis for Effective Anomaly--Based Intrusion Detection on NSL-KDD{J}. International Journal of Engineering Science & Technology,2010,2(6):3175--3180

Google Scholar

[7]

Hinton G E,Osindero S,Teh Y W.A Fast Learning Algorithm for Deep Belief Nets{J}. Neural Computation,2006, 18(7):1527--1554.

Digital Library

Google Scholar

[8]

Hinton G E.Training Products of Experts by Minimizing Contrastive Divergence{J}. Neural Computation,2002,14(8):1771--1800.

Digital Library

Google Scholar

[9]

Hinton G E.A practical guide to training restricted Boltzmann machines{J}. Momentum,2010,9(1):926.

Google Scholar

[10]

Rumehart D E,Hinton G E,Williams R J.Learning representations by back-propagating errors{J}. Nature,1986,323(6088):533--536.

Google Scholar

[11]

NSL-KDD dataset,http://nsl.cs.unb.ca/NSL-KDD/.

Google Scholar

[12]

LIN C J.Formulations of Support Vector Machines:A Note from an Optimization Point of View{J}. Neural Computation,2001,13(2):307--317

Digital Library

Google Scholar

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Published In

ICNCC '17: Proceedings of the 2017 VI International Conference on Network, Communication and Computing

December 2017

265 pages

ISBN:9781450353663

DOI:10.1145/3171592

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Published: 08 December 2017

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ICNCC 2017: 2017 VI International Conference on Network, Communication and Computing

December 8 - 10, 2017

Kunming, China

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