Deep Learning Method for Denial of Service Attack Detection Based on Restricted Boltzmann Machine.

08:00 EDT 1st June 2018 | BioPortfolio

Summary of "Deep Learning Method for Denial of Service Attack Detection Based on Restricted Boltzmann Machine."

In this article, the application of the deep learning method based on Gaussian-Bernoulli type restricted Boltzmann machine (RBM) to the detection of denial of service (DoS) attacks is considered. To increase the DoS attack detection accuracy, seven additional layers are added between the visible and the hidden layers of the RBM. Accurate results in DoS attack detection are obtained by optimization of the hyperparameters of the proposed deep RBM model. The form of the RBM that allows application of the continuous data is used. In this type of RBM, the probability distribution of the visible layer is replaced by a Gaussian distribution. Comparative analysis of the accuracy of the proposed method with Bernoulli-Bernoulli RBM, Gaussian-Bernoulli RBM, deep belief network type deep learning methods on DoS attack detection is provided. Detection accuracy of the methods is verified on the NSL-KDD data set. Higher accuracy from the proposed multilayer deep Gaussian-Bernoulli type RBM is obtained.


Journal Details

This article was published in the following journal.

Name: Big data
ISSN: 2167-647X
Pages: 159-169


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