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Article

Measuring the Impact of Accurate Feature Selection on the Performance of RBM in Comparison to State of the Art Machine Learning Algorithms

1
Distributed Analytics and Security Institute, High-Performance Computing Collaboratory, Mississippi State University, Mississippi State, MS 39762, USA
2
Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
3
Department of Physics, University of Colombo, Colombo 00300, Sri Lanka
4
Department of Physics and Astronomy, Mississippi State University, Mississippi State, MS 39762, USA
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(7), 1167; https://doi.org/10.3390/electronics9071167
Received: 6 June 2020 / Revised: 14 July 2020 / Accepted: 16 July 2020 / Published: 18 July 2020
The amassed growth in the size of data, caused by the advancement of technologies and the use of internet of things to collect and transmit data, resulted in the creation of large volumes of data and an increasing variety of data types that need to be processed at very high speeds so that we can extract meaningful information from these massive volumes of unstructured data. The process of mining this data is very challenging since a lot of the data suffers from the problem of high dimensionality. The quandary of high dimensionality represents a great challenge that can be controlled through the process of feature selection. Feature selection is a complex task with multiple layers of difficulty. To be able to grasp and realize the impediments associated with high dimensional data a more and in-depth understanding of feature selection is required. In this study, we examine the effect of appropriate feature selection during the classification process of anomaly network intrusion detection systems. We test its effect on the performance of Restricted Boltzmann Machines and compare its performance to conventional machine learning algorithms. We establish that when certain features that are representative of the model are to be selected the change in the accuracy was always less than 3% across all algorithms. This verifies that the accurate selection of the important features when building a model can have a significant impact on the accuracy level of the classifiers. We also confirmed in this study that the performance of the Restricted Boltzmann Machines can outperform or at least is comparable to other well-known machine learning algorithms. Extracting those important features can be very useful when trying to build a model with datasets with a lot of features. View Full-Text
Keywords: anomaly network intrusion detection systems; machine learning; restricted boltzmann machine; ISCX dataset; NetFlow traffic; cybersecurity anomaly network intrusion detection systems; machine learning; restricted boltzmann machine; ISCX dataset; NetFlow traffic; cybersecurity
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MDPI and ACS Style

Aldwairi, T.; Perera, D.; Novotny, M.A. Measuring the Impact of Accurate Feature Selection on the Performance of RBM in Comparison to State of the Art Machine Learning Algorithms. Electronics 2020, 9, 1167. https://doi.org/10.3390/electronics9071167

AMA Style

Aldwairi T, Perera D, Novotny MA. Measuring the Impact of Accurate Feature Selection on the Performance of RBM in Comparison to State of the Art Machine Learning Algorithms. Electronics. 2020; 9(7):1167. https://doi.org/10.3390/electronics9071167

Chicago/Turabian Style

Aldwairi, Tamer, Dilina Perera, and Mark A. Novotny 2020. "Measuring the Impact of Accurate Feature Selection on the Performance of RBM in Comparison to State of the Art Machine Learning Algorithms" Electronics 9, no. 7: 1167. https://doi.org/10.3390/electronics9071167

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