article

Intrusion Detection Using Deep Belief Network and Extreme Learning Machine

Authors:

Venkata Ramesh Bontupalli,

Tarek M. TahaAuthors Info & Claims

International Journal of Monitoring and Surveillance Technologies Research, Volume 3, Issue 2

Pages 35 - 56

https://doi.org/10.4018/IJMSTR.2015040103

Published: 01 April 2015 Publication History

Abstract

Security threats for computer networks have increased dramatically over the last decade, becoming bolder and more brazen. There is a strong need for effective Intrusion Detection Systems IDS that are designed to interpret intrusion attempts in incoming network traffic intelligently. In this paper, the authors explored the capabilities of Deep Belief Networks DBN-one of the most influential deep learning approach-in performing intrusion detection after training with the NSL-KDD dataset. Additionally, they examined the impact of using Extreme Learning Machine ELM and Regularized ELM on the same dataset to evaluate the performance against DBN and Support Vector Machine SVM approaches. The trained system identifies any type of unknown attack in the dataset examined. In addition to detecting attacks, the proposed system also classifies them into five groups. The implementation with DBN and SVM give a testing accuracy of about 97.5% and 88.33% respectively with 40% of training data selected from the NSL-KDD dataset. On the other hand, the experimental results show around 98.20% and 98.26% testing accuracy respectively for ELM and RELM after reducing the data dimensions from 41 to 9 essential features with 40% training data. ELM and RELM perform better in terms of testing accuracy upon comparison with DBN and SVM.

References

[1]

Ali, A. O. 2010. Multilayer perceptrons networks for an Intelligent Adaptive intrusion detection system. IJCSNS, 102, 275.

[2]

Ambwani, T. 2003, July. Multi class support vector machine implementation to intrusion detection. Proceedings of the International Joint Conference on Neural Networks Vol. 3, pp. 2300-2305. IEEE. 10.1109/IJCNN.2003.1223770

[3]

AlomM. Z.SidikeP.AsariV.TahaT. M. 2015. State Preserving Extreme Learning Machine for Face Recognition. Proc. of IEEE International Joint Conference on Neural Networks pp. 1-7.

[4]

Bontupalli, V., Hasan, R., & Taha, T. M. 2014, June. Power efficient architecture for network intrusion detection system. Proceedings of the IEEE National Aerospace and Electronics Conference NAECON '14 pp. 250-254. IEEE. 10.1109/NAECON.2014.7045811

[5]

Chairunnisa, L., & Widiputra, H. D. 2009. Clustering Based Intrusion Detection For Network Profiling Using K-Means, ECM AND K-Nearest Neighbor Algorithm. Konferensi Nasional Sistem dan Informatika.

[6]

Deng, W., Zheng, Q., & Chen, L. 2009, March. Regularized extreme learning machine. Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining CIDM '09 pp. 389-395. IEEE. 10.1109/CIDM.2009.4938676

[7]

Faraoun, K. M., & Boukelif, A. 2006. Neural networks learning improvement using the K-means clustering algorithm to detect network intrusions. INFOCOMP Journal of Computer Science, 53, 28-36.

[8]

Feng, G., Huang, G. B., Lin, Q., & Gay, R. 2009. Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Transactions on Neural Networks, 208, 1352-1357.

Digital Library

[9]

Giroire, F., Chandrashekar, J., Iannaccone, G., Papagiannaki, K., Schooler, E. M., & Taft, N. 2008. The cubicle vs. the coffee shop: Behavioral modes in enterprise end-users. In Passive and Active Network Measurement pp. 202-211. Springer Berlin Heidelberg.

[10]

GörnitzN.KloftM.RieckK.BrefeldU. 2009, November. Active learning for network intrusion detection. Proceedings of the 2nd ACM workshop on Security and artificial intelligence pp. 47-54. ACM. 10.1145/1654988.1655002

[11]

Freund, Y., & Haussler, D. 1994. Unsupervised learning of distributions on binary vectors using two layer networks Technical Report. University of California at Santa Cruz.

[12]

Hinton, G. E. 2002. Training products of experts by minimizing contrastive divergence. Neural Computation, 148, 1771-1800. 12180402.

Digital Library

[13]

Hinton, G. E., Osindero, S., & Teh, Y. W. 2006. A fast learning algorithm for deep belief nets. Neural Computation, 187, 1527-1554. 16764513.

Digital Library

[14]

Huang, G. B., Zhu, Q. Y., & Siew, C. K. 2006. Extreme learning machine: Theory and applications. Neurocomputing, 701-3, 489-501.

[15]

Huang, G. B., Wang, D. H., & Lan, Y. 2011. Extreme learning machines: A survey. International Journal of Machine Learning and Cybernetics, 22, 107-122.

[16]

Huang, G. B., Chen, L., & Siew, C. K. 2006. Universal approximation using incremental constructive feedforward networks with random hidden nodes. . IEEE Transactions on Neural Networks, 174, 879-892.

Digital Library

[17]

Huang, G. B., Zhou, H., Ding, X., & Zhang, R. 2012. Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 422, 513-529.

Digital Library

[18]

KDD'99 dataset. 2010. Retrieved from http://kdd.ics.uci.edu/databases

[19]

KloftM.BrefeldU.DüesselP.GehlC.LaskovP. 2008, October. Automatic feature selection for anomaly detection. Proceedings of the 1st ACM workshop on Workshop on AISec pp. 71-76. ACM. 10.1145/1456377.1456395

[20]

LarochelleH.BengioY. 2008, July. Classification using discriminative restricted Boltzmann machines. Proceedings of the 25th international conference on Machine learning pp. 536-543. ACM.

[21]

Larochelle, H., Bengio, Y., Louradour, J., & Lamblin, P. 2009. Exploring strategies for training deep neural networks. Journal of Machine Learning Research, 10, 1-40.

Digital Library

[22]

Lazarevic, A., Kumar, V., & Srivastava, J. 2005. Intrusion detection: A survey. In Managing Cyber Threats pp. 19-78. Springer US.

[23]

Liao, Y., & Vemuri, V. R. 2002. Use of k-nearest neighbor classifier for intrusion detection. Computers & Security, 215, 439-448.

[24]

LuY.CohenI.ZhouX. S.TianQ. 2007, September. Feature selection using principal feature analysis. Proceedings of the 15th international conference on Multimedia pp. 301-304. ACM. 10.1145/1291233.1291297

[25]

Marchette, D. J. 1999, April. A Statistical Method for Profiling Network Traffic. Proceedings of the Workshop on Intrusion Detection and Network Monitoring pp. 119-128.

[26]

McAfee, L. 2008. Document Classification using Deep Belief Nets. CS224n. Sprint.

[27]

Salama, M. A., Eid, H. F., Ramadan, R. A., Darwish, A., & Hassanien, A. E. 2011. Hybrid Intelligent Intrusion Detection Scheme. Advances in Intelligent and Soft Computing, 96, 293-303.

[28]

Mukkamala, S., Janoski, G., & Sung, A. 2002. Intrusion detection using neural networks and support vector machines. Proceedings of the 2002 International Joint Conference on Neural Networks IJCNN '02 Vol. 2, pp. 1702-1707. IEEE. 10.1109/IJCNN.2002.1007774

[29]

Noulas, A. K., & Krse, B. J. A. 2008, October. Deep Belief Networks for dimensionality reduction. Proceedings of the Belgian-Dutch Conference on Artificial Intelligence, Netherland.

[30]

PillaiM. M.EloffJ. H.VenterH. S. 2004, October. An approach to implement a network intrusion detection system using genetic algorithms. Proceedings of the 2004 annual research conference of the South African institute of computer scientists and information technologists on IT research in developing countries pp. 221-221. South African Institute for Computer Scientists and Information Technologists.

[31]

Smolensky, P. 1986. Information processing in dynamical systems: Foundations of harmony theory.

[32]

ZaneroS.SavaresiS. M. 2004, March. Unsupervised learning techniques for an intrusion detection system. Proceedings of the 2004 ACM symposium on Applied computing pp. 412-419. ACM. 10.1145/967900.967988

Cited By

Du RLi RZhang Z(2023)Ensemble Two Stage Machine Learning for Network Abnormal DetectionProceedings of the 2023 15th International Conference on Machine Learning and Computing10.1145/3587716.3587732(97-102)Online publication date: 17-Feb-2023
https://dl.acm.org/doi/10.1145/3587716.3587732
Chao SYang GNie MLiu YZhang M(2022)Full-Rotation Quantum Convolutional Neural Network for Abnormal Intrusion Detection SystemProceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition10.1145/3573942.3574105(852-859)Online publication date: 23-Sep-2022
https://dl.acm.org/doi/10.1145/3573942.3574105
Kwon DKim HKim JSuh SKim IKim K(2019)A survey of deep learning-based network anomaly detectionCluster Computing10.1007/s10586-017-1117-822:1(949-961)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1007/s10586-017-1117-8
Show More Cited By

Intrusion Detection Using Deep Belief Network and Extreme Learning Machine
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

Recommendations

An Intrusion Detection Model Based on Deep Belief Networks
CBD '14: Proceedings of the 2014 Second International Conference on Advanced Cloud and Big Data

This paper focuses on an important research problem of Big Data classification in intrusion detection system. Deep Belief Networks is introduced to the field of intrusion detection, and an intrusion detection model based on Deep Belief Networks is ...
Intrusion detection algorithom based on transfer extreme learning machine

Intrusion detection can effectively detect malicious attacks in computer networks, which has always been a research hotspot in field of network security. At present, most of the existing intrusion detection methods are based on traditional machine ...
Network Intrusion Detection: Automated and Manual Methods Prone to Attack and Evasion

In this article, the authors describe common intrusion detection techniques, NIDS evasion methods, and how NIDSs detect intrusions. Additionally, we introduce new evasion methods, present test results for confirming attack outcomes based on server ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image International Journal of Monitoring and Surveillance Technologies Research

International Journal of Monitoring and Surveillance Technologies Research Volume 3, Issue 2

April 2015

91 pages

ISSN:2166-7241

EISSN:2166-725X

Issue’s Table of Contents

Publisher

IGI Global

United States

Publication History

Published: 01 April 2015

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Du RLi RZhang Z(2023)Ensemble Two Stage Machine Learning for Network Abnormal DetectionProceedings of the 2023 15th International Conference on Machine Learning and Computing10.1145/3587716.3587732(97-102)Online publication date: 17-Feb-2023
https://dl.acm.org/doi/10.1145/3587716.3587732
Chao SYang GNie MLiu YZhang M(2022)Full-Rotation Quantum Convolutional Neural Network for Abnormal Intrusion Detection SystemProceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition10.1145/3573942.3574105(852-859)Online publication date: 23-Sep-2022
https://dl.acm.org/doi/10.1145/3573942.3574105
Kwon DKim HKim JSuh SKim IKim K(2019)A survey of deep learning-based network anomaly detectionCluster Computing10.1007/s10586-017-1117-822:1(949-961)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1007/s10586-017-1117-8
Bu SCho S(2019)Genetic Algorithm-Based Deep Learning Ensemble for Detecting Database Intrusion via Insider AttackHybrid Artificial Intelligent Systems10.1007/978-3-030-29859-3_13(145-156)Online publication date: 4-Sep-2019
https://dl.acm.org/doi/10.1007/978-3-030-29859-3_13
Tian JLi P(2017)An Intrusion Detection Algorithm of Dynamic Recursive Deep Belief NetworksProceedings of the 2017 International Conference on Information Technology10.1145/3176653.3176717(180-183)Online publication date: 27-Dec-2017
https://dl.acm.org/doi/10.1145/3176653.3176717

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents