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.

中文翻译：

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基于受限玻尔兹曼机的拒绝服务攻击检测深度学习方法

本文考虑了基于高斯-伯努利类型受限玻尔兹曼机（RBM）的深度学习方法在拒绝服务（DoS）攻击检测中的应用。为了提高DoS攻击检测的准确性，在RBM的可见层和隐藏层之间添加了七个附加层。通过优化所提出的深度RBM模型的超参数，可以获得准确的DoS攻击检测结果。使用允许应用连续数据的RBM形式。在这种类型的RBM中，可见层的概率分布被高斯分布代替。比较了该方法与Bernoulli-Bernoulli RBM，Gaussian-Bernoulli RBM，深度信念网络型深度学习方法在DoS攻击检测中的准确性。该方法的检测准确性已在NSL-KDD数据集上得到验证。从提出的多层深高斯-伯努利型RBM获得更高的精度。