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A Multi-Scale Feature Attention Approach to Network Traffic Classification and Its Model Explanation.

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Titel: A Multi-Scale Feature Attention Approach to Network Traffic Classification and Its Model Explanation.
Autoren: Wang, Yipeng, Yun, Xiaochun, Zhang, Yongzheng, Zhao, Chen, Liu, Xin
Quelle: IEEE Transactions on Network & Service Management; Jun2022, Vol. 19 Issue 2, p875-889, 15p
Abstract: Network traffic classification, the task of associating network traffic with their generating application protocols or applications, is valuable for the control, allocation, and management of resources in today’s TCP/IP networks. In this paper, we propose Ulfar, a multi-scale feature attention approach to network traffic classification, which uses convolutional neural networks (CNN) as the building block of the deep packet analysis model. In Ulfar, we take only one packet per flow for network traffic classification. Ulfar is based on the key insight that format-related bytes appear at fixed offsets or in a specific pattern in the IP packet, and these format-related bytes are important for accurate network traffic classification. Our neural network model can automatically recover the format-related bytes by building high-level, multi-scale ${n}$ -gram features from raw byte sequences. In addition, at the representation learning side, we try to understand what patterns and signatures our neural network model learns from network traffic. We evaluate Ulfar using two publicly available datasets, and our experimental results show that Ulfar can conduct accurate network traffic classification. Also, we compare the results of Ulfar with four state-of-the-art approaches, and find that Ulfar has the ability to classify network traffic more accurately. [ABSTRACT FROM AUTHOR]
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  Data: A Multi-Scale Feature Attention Approach to Network Traffic Classification and Its Model Explanation.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Yipeng%22">Wang, Yipeng</searchLink><br /><searchLink fieldCode="AR" term="%22Yun%2C+Xiaochun%22">Yun, Xiaochun</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yongzheng%22">Zhang, Yongzheng</searchLink><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Chen%22">Zhao, Chen</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Xin%22">Liu, Xin</searchLink>
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  Data: IEEE Transactions on Network & Service Management; Jun2022, Vol. 19 Issue 2, p875-889, 15p
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Network traffic classification, the task of associating network traffic with their generating application protocols or applications, is valuable for the control, allocation, and management of resources in today’s TCP/IP networks. In this paper, we propose Ulfar, a multi-scale feature attention approach to network traffic classification, which uses convolutional neural networks (CNN) as the building block of the deep packet analysis model. In Ulfar, we take only one packet per flow for network traffic classification. Ulfar is based on the key insight that format-related bytes appear at fixed offsets or in a specific pattern in the IP packet, and these format-related bytes are important for accurate network traffic classification. Our neural network model can automatically recover the format-related bytes by building high-level, multi-scale ${n}$ -gram features from raw byte sequences. In addition, at the representation learning side, we try to understand what patterns and signatures our neural network model learns from network traffic. We evaluate Ulfar using two publicly available datasets, and our experimental results show that Ulfar can conduct accurate network traffic classification. Also, we compare the results of Ulfar with four state-of-the-art approaches, and find that Ulfar has the ability to classify network traffic more accurately. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of IEEE Transactions on Network & Service Management is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jun2022
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