Malicious traffic detection combined deep neural network with hierarchical attention mechanism

Abstract Given the gradual intensification of the current network security situation, malicious attack traffic is flooding the entire network environment, and the current malicious traffic detection model is insufficient in detection efficiency and detection performance. This paper proposes a data p...

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Autores principales:	Xiaoyang Liu, Jiamiao Liu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4b64aa99e423495497fddadaf0a1dfd6
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spelling	oai:doaj.org-article:4b64aa99e423495497fddadaf0a1dfd62021-12-02T17:30:46ZMalicious traffic detection combined deep neural network with hierarchical attention mechanism10.1038/s41598-021-91805-z2045-2322https://doaj.org/article/4b64aa99e423495497fddadaf0a1dfd62021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91805-zhttps://doaj.org/toc/2045-2322Abstract Given the gradual intensification of the current network security situation, malicious attack traffic is flooding the entire network environment, and the current malicious traffic detection model is insufficient in detection efficiency and detection performance. This paper proposes a data processing method that divides the flow data into data flow segments so that the model can improve the throughput per unit time to meet its detection efficiency. For this kind of data, a malicious traffic detection model with a hierarchical attention mechanism is also proposed and named HAGRU (Hierarchical Attention Gated Recurrent Unit). By fusing the feature information of the three hierarchies, the detection ability of the model is improved. An attention mechanism is introduced to focus on malicious flows in the data flow segment, which can reasonably utilize limited computing resources. Finally, compare the proposed model with the current state of the method on the datasets. The experimental results show that: the novel model performs well in different evaluation indicators (detection rate, false-positive rate, F-score), and it can improve the performance of category recognition with fewer samples when the data is unbalanced. At the same time, the training of the novel model on larger datasets will enhance the generalization ability and reduce the false alarm rate. The proposed model not only improves the performance of malicious traffic detection but also provides a new research method for improving the efficiency of model detection.Xiaoyang LiuJiamiao LiuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xiaoyang Liu Jiamiao Liu Malicious traffic detection combined deep neural network with hierarchical attention mechanism
description	Abstract Given the gradual intensification of the current network security situation, malicious attack traffic is flooding the entire network environment, and the current malicious traffic detection model is insufficient in detection efficiency and detection performance. This paper proposes a data processing method that divides the flow data into data flow segments so that the model can improve the throughput per unit time to meet its detection efficiency. For this kind of data, a malicious traffic detection model with a hierarchical attention mechanism is also proposed and named HAGRU (Hierarchical Attention Gated Recurrent Unit). By fusing the feature information of the three hierarchies, the detection ability of the model is improved. An attention mechanism is introduced to focus on malicious flows in the data flow segment, which can reasonably utilize limited computing resources. Finally, compare the proposed model with the current state of the method on the datasets. The experimental results show that: the novel model performs well in different evaluation indicators (detection rate, false-positive rate, F-score), and it can improve the performance of category recognition with fewer samples when the data is unbalanced. At the same time, the training of the novel model on larger datasets will enhance the generalization ability and reduce the false alarm rate. The proposed model not only improves the performance of malicious traffic detection but also provides a new research method for improving the efficiency of model detection.
format	article
author	Xiaoyang Liu Jiamiao Liu
author_facet	Xiaoyang Liu Jiamiao Liu
author_sort	Xiaoyang Liu
title	Malicious traffic detection combined deep neural network with hierarchical attention mechanism
title_short	Malicious traffic detection combined deep neural network with hierarchical attention mechanism
title_full	Malicious traffic detection combined deep neural network with hierarchical attention mechanism
title_fullStr	Malicious traffic detection combined deep neural network with hierarchical attention mechanism
title_full_unstemmed	Malicious traffic detection combined deep neural network with hierarchical attention mechanism
title_sort	malicious traffic detection combined deep neural network with hierarchical attention mechanism
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4b64aa99e423495497fddadaf0a1dfd6
work_keys_str_mv	AT xiaoyangliu malicioustrafficdetectioncombineddeepneuralnetworkwithhierarchicalattentionmechanism AT jiamiaoliu malicioustrafficdetectioncombineddeepneuralnetworkwithhierarchicalattentionmechanism
_version_	1718380727789158400

Malicious traffic detection combined deep neural network with hierarchical attention mechanism

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