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Article

SAAE-DNN: Deep Learning Method on Intrusion Detection

1
College of Software, Xinjiang University, Urumqi 830000, China
2
College of Information Science and Engineering, Xinjiang University, Urumqi 830000, China
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(10), 1695; https://doi.org/10.3390/sym12101695
Received: 16 September 2020 / Revised: 9 October 2020 / Accepted: 12 October 2020 / Published: 15 October 2020
(This article belongs to the Section Computer and Engineering Science and Symmetry/Asymmetry)
Intrusion detection system (IDS) plays a significant role in preventing network attacks and plays a vital role in the field of national security. At present, the existing intrusion detection methods are generally based on traditional machine learning models, such as random forest and decision tree, but they rely heavily on artificial feature extraction and have relatively low accuracy. To solve the problems of feature extraction and low detection accuracy in intrusion detection, an intrusion detection model SAAE-DNN, based on stacked autoencoder (SAE), attention mechanism and deep neural network (DNN), is proposed. The SAE represents data with a latent layer, and the attention mechanism enables the network to obtain the key features of intrusion detection. The trained SAAE encoder can not only automatically extract features, but also initialize the weights of DNN potential layers to improve the detection accuracy of DNN. We evaluate the performance of SAAE-DNN in binary-classification and multi-classification on an NSL-KDD dataset. The SAAE-DNN model can detect normally and attack symmetrically, with an accuracy of 87.74% and 82.14% (binary-classification and multi-classification), which is higher than that of machine learning methods such as random forest and decision tree. The experimental results show that the model has a better performance than other comparison methods. View Full-Text
Keywords: intrusion detection; attention mechanism; stacked autoencoder; DNN; classification; NSL-KDD; deep learning intrusion detection; attention mechanism; stacked autoencoder; DNN; classification; NSL-KDD; deep learning
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MDPI and ACS Style

Tang, C.; Luktarhan, N.; Zhao, Y. SAAE-DNN: Deep Learning Method on Intrusion Detection. Symmetry 2020, 12, 1695. https://doi.org/10.3390/sym12101695

AMA Style

Tang C, Luktarhan N, Zhao Y. SAAE-DNN: Deep Learning Method on Intrusion Detection. Symmetry. 2020; 12(10):1695. https://doi.org/10.3390/sym12101695

Chicago/Turabian Style

Tang, Chaofei, Nurbol Luktarhan, and Yuxin Zhao. 2020. "SAAE-DNN: Deep Learning Method on Intrusion Detection" Symmetry 12, no. 10: 1695. https://doi.org/10.3390/sym12101695

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