Menyu
ITI
əməkdaslarının elmi isləri
Elektron kitabxana
Konfranslar İnformasiya Sistemi
Qəzetlər
UOT 004
|
ITI əməkdaşlarının elmi işləri - məqalə |
Biblioqrafik təsvir | Imamverdiyev , Y.N. Deep Learning Method for Denial of Service Attack Detection Based on Restricted Boltzmann Machine / Y.N. Imamverdiyev , F.D. Abdullayeva // Big Data. - 2018. - N: 2, Volume 6. - P. 159-169. | Annotasiya | 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. | Elektron variant | Elektron variant |
|
________
© ict.az http://ict.az/az
|
|