Deep packet: a novel approach for encrypted traffic classification using deep learning

Network traffic classification has become more important with the rapid growth of Internet and online applications. Numerous studies have been done on this topic which have led to many different approaches. Most of these approaches use predefined features extracted by an expert in order to classify...

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Veröffentlicht in:	Soft computing (Berlin, Germany) Jg. 24; H. 3; S. 1999 - 2012
Hauptverfasser:	Lotfollahi, Mohammad, Jafari Siavoshani, Mahdi, Shirali Hossein Zade, Ramin, Saberian, Mohammdsadegh
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2020 Springer Nature B.V
Schlagworte:	Access control Accuracy Artificial Intelligence Artificial neural networks Classification Communications traffic Computational Intelligence Control Data encryption Datasets Deep learning Engineering Feature extraction Identification Internet service providers Machine learning Mathematical Logic and Foundations Mechatronics Methodologies and Application Methods Neural networks Privacy Protocol Robotics Statistical analysis Streaming media Virtual private networks Deep learning Stacked autoencoder Deep Packet Application identification Convolutional neural networks Network traffic classification Traffic characterization
ISSN:	1432-7643, 1433-7479
Online-Zugang:	Volltext
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Abstract	Network traffic classification has become more important with the rapid growth of Internet and online applications. Numerous studies have been done on this topic which have led to many different approaches. Most of these approaches use predefined features extracted by an expert in order to classify network traffic. In contrast, in this study, we propose a deep learning -based approach which integrates both feature extraction and classification phases into one system. Our proposed scheme, called “Deep Packet,” can handle both traffic characterization in which the network traffic is categorized into major classes (e.g., FTP and P2P) and application identification in which identifying end-user applications (e.g., BitTorrent and Skype) is desired. Contrary to most of the current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic. The Deep Packet framework employs two deep neural network structures, namely stacked autoencoder (SAE) and convolution neural network (CNN) in order to classify network traffic. Our experiments show that the best result is achieved when Deep Packet uses CNN as its classification model where it achieves recall of 0.98 in application identification task and 0.94 in traffic categorization task. To the best of our knowledge, Deep Packet outperforms all of the proposed classification methods on UNB ISCX VPN-nonVPN dataset.
AbstractList	Network traffic classification has become more important with the rapid growth of Internet and online applications. Numerous studies have been done on this topic which have led to many different approaches. Most of these approaches use predefined features extracted by an expert in order to classify network traffic. In contrast, in this study, we propose a deep learning-based approach which integrates both feature extraction and classification phases into one system. Our proposed scheme, called “Deep Packet,” can handle both traffic characterization in which the network traffic is categorized into major classes (e.g., FTP and P2P) and application identification in which identifying end-user applications (e.g., BitTorrent and Skype) is desired. Contrary to most of the current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic. The Deep Packet framework employs two deep neural network structures, namely stacked autoencoder (SAE) and convolution neural network (CNN) in order to classify network traffic. Our experiments show that the best result is achieved when Deep Packet uses CNN as its classification model where it achieves recall of 0.98 in application identification task and 0.94 in traffic categorization task. To the best of our knowledge, Deep Packet outperforms all of the proposed classification methods on UNB ISCX VPN-nonVPN dataset. Network traffic classification has become more important with the rapid growth of Internet and online applications. Numerous studies have been done on this topic which have led to many different approaches. Most of these approaches use predefined features extracted by an expert in order to classify network traffic. In contrast, in this study, we propose a deep learning -based approach which integrates both feature extraction and classification phases into one system. Our proposed scheme, called “Deep Packet,” can handle both traffic characterization in which the network traffic is categorized into major classes (e.g., FTP and P2P) and application identification in which identifying end-user applications (e.g., BitTorrent and Skype) is desired. Contrary to most of the current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic. The Deep Packet framework employs two deep neural network structures, namely stacked autoencoder (SAE) and convolution neural network (CNN) in order to classify network traffic. Our experiments show that the best result is achieved when Deep Packet uses CNN as its classification model where it achieves recall of 0.98 in application identification task and 0.94 in traffic categorization task. To the best of our knowledge, Deep Packet outperforms all of the proposed classification methods on UNB ISCX VPN-nonVPN dataset.
Author	Saberian, Mohammdsadegh Shirali Hossein Zade, Ramin Lotfollahi, Mohammad Jafari Siavoshani, Mahdi
Author_xml	– sequence: 1 givenname: Mohammad orcidid: 0000-0001-6858-7985 surname: Lotfollahi fullname: Lotfollahi, Mohammad organization: Sharif University of Technology – sequence: 2 givenname: Mahdi orcidid: 0000-0003-3860-5999 surname: Jafari Siavoshani fullname: Jafari Siavoshani, Mahdi email: mjafari@sharif.edu organization: Sharif University of Technology – sequence: 3 givenname: Ramin surname: Shirali Hossein Zade fullname: Shirali Hossein Zade, Ramin organization: Sharif University of Technology – sequence: 4 givenname: Mohammdsadegh surname: Saberian fullname: Saberian, Mohammdsadegh organization: Sharif University of Technology
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References_xml	– reference: Lotfollahi M, Shirali Hossein Zade R, Jafari Siavoshani M, Saberian M (2017) Deep packet: a novel approach for encrypted traffic classification using deep learning. CoRR abs/1709.02656. arXiv:1709.02656 – reference: Madhukar A, Williamson C (2006) A longitudinal study of p2p traffic classification. In: Modeling, analysis, and simulation of computer and telecommunication systems, 2006. MASCOTS 2006. 14th IEEE international symposium on, IEEE, pp 179–188 – reference: Chollet F et al (2017) Keras. https://github.com/fchollet/keras – reference: MontavonGSamekWMüllerKRMethods for interpreting and understanding deep neural networksDigit Signal Process201873115373787010.1016/j.dsp.2017.10.011 – reference: Sun R, Yang B, Peng L, Chen Z, Zhang L, Jing S (2010) Traffic classification using probabilistic neural networks. 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Snippet	Network traffic classification has become more important with the rapid growth of Internet and online applications. Numerous studies have been done on this...
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SubjectTerms	Access control Accuracy Artificial Intelligence Artificial neural networks Classification Communications traffic Computational Intelligence Control Data encryption Datasets Deep learning Engineering Feature extraction Identification Internet service providers Machine learning Mathematical Logic and Foundations Mechatronics Methodologies and Application Methods Neural networks Privacy Protocol Robotics Statistical analysis Streaming media Virtual private networks
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Title	Deep packet: a novel approach for encrypted traffic classification using deep learning
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