An evaluation of the performance of Restricted Boltzmann Machines as a model for anomaly network intrusion detection

Aldwairi, Tamer; Perera, Dilina; Novotny, Mark A.

doi:10.1016/j.comnet.2018.07.025

Title: An evaluation of the performance of Restricted Boltzmann Machines as a model for anomaly network intrusion detection

Journal Article · 30 September 2018 · Computer Networks

DOI: https://doi.org/10.1016/j.comnet.2018.07.025 · OSTI ID:1464275

Aldwairi, Tamer; Perera, Dilina; Novotny, Mark A.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 1464275

Journal Information:: Computer Networks, Journal Name: Computer Networks Journal Issue: C Vol. 144; ISSN 1389-1286

Publisher:: ElsevierCopyright Statement

Country of Publication:: Netherlands

Language:: English

References (15)

Training Restricted Boltzmann Machines with Multi-tempering: Harnessing Parallelization Brakel, Philemon; Dieleman, Sander; Schrauwen, Benjamin Artificial Neural Networks and Machine Learning – ICANN 2012 https://doi.org/10.1007/978-3-642-33266-1_12	book	January 2012
The general inefficiency of batch training for gradient descent learning Wilson, D. Randall; Martinez, Tony R. Neural Networks, Vol. 16, Issue 10 https://doi.org/10.1016/S0893-6080(03)00138-2	journal	December 2003
Anomaly-based network intrusion detection: Techniques, systems and challenges García-Teodoro, P.; Díaz-Verdejo, J.; Maciá-Fernández, G. Computers & Security, Vol. 28, Issue 1-2 https://doi.org/10.1016/j.cose.2008.08.003	journal	February 2009
Toward developing a systematic approach to generate benchmark datasets for intrusion detection Shiravi, Ali; Shiravi, Hadi; Tavallaee, Mahbod Computers & Security, Vol. 31, Issue 3 https://doi.org/10.1016/j.cose.2011.12.012	journal	May 2012
Network anomaly detection with the restricted Boltzmann machine Fiore, Ugo; Palmieri, Francesco; Castiglione, Aniello Neurocomputing, Vol. 122 https://doi.org/10.1016/j.neucom.2012.11.050	journal	December 2013
Training restricted Boltzmann machines: An introduction Fischer, Asja; Igel, Christian Pattern Recognition, Vol. 47, Issue 1 https://doi.org/10.1016/j.patcog.2013.05.025	journal	January 2014
Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System Albayati, Mohanad; Issac, Biju International Journal of Computational Intelligence Systems, Vol. 8, Issue 5 https://doi.org/10.1080/18756891.2015.1084705	journal	September 2015
Intrusion Detection in 802.11 Networks: Empirical Evaluation of Threats and a Public Dataset Kolias, Constantinos; Kambourakis, Georgios; Stavrou, Angelos IEEE Communications Surveys & Tutorials, Vol. 18, Issue 1 https://doi.org/10.1109/COMST.2015.2402161	journal	April 2016
Intrusion detection using deep belief networks Alom, Md. Zahangir; Bontupalli, VenkataRamesh; Taha, Tarek M. NAECON 2015 - IEEE National Aerospace and Electronics Conference, 2015 National Aerospace and Electronics Conference (NAECON) https://doi.org/10.1109/NAECON.2015.7443094	conference	June 2015
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images Geman, Stuart; Geman, Donald IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-6, Issue 6 https://doi.org/10.1109/TPAMI.1984.4767596	journal	November 1984
Classification using discriminative restricted Boltzmann machines Larochelle, Hugo; Bengio, Yoshua Proceedings of the 25th international conference on Machine learning - ICML '08 https://doi.org/10.1145/1390156.1390224	conference	January 2008
The WEKA data mining software: an update Hall, Mark; Frank, Eibe; Holmes, Geoffrey ACM SIGKDD Explorations Newsletter, Vol. 11, Issue 1 https://doi.org/10.1145/1656274.1656278	journal	November 2009
Training Products of Experts by Minimizing Contrastive Divergence Hinton, Geoffrey E. Neural Computation, Vol. 14, Issue 8 https://doi.org/10.1162/089976602760128018	journal	August 2002
A Learning Algorithm for Boltzmann Machines* Ackley, David H.; Hinton, Geoffrey E.; Sejnowski, Terrence J. Cognitive Science, Vol. 9, Issue 1 https://doi.org/10.1207/s15516709cog0901_7	journal	January 1985
Learning Deep Architectures for AI Bengio, Y. Foundations and Trends® in Machine Learning, Vol. 2, Issue 1 https://doi.org/10.1561/2200000006	journal	January 2009

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