ICLSTM: Encrypted Traffic Service Identification Based on Inception-LSTM Neural Network

The wide application of encryption technology has made traffic classification gradually become a major challenge in the field of network security. Traditional methods such as machine learning, which rely heavily on feature engineering and others, can no longer fully meet the needs of encrypted traff...
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Autor*in:	Bei Lu [verfasserIn] Nurbol Luktarhan [verfasserIn] Chao Ding [verfasserIn] Wenhui Zhang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	encrypted traffic service identification neural network inception LSTM

Übergeordnetes Werk:	In: Symmetry - MDPI AG, 2009, 13(2021), 6, p 1080
Übergeordnetes Werk:	volume:13 ; year:2021 ; number:6, p 1080

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/sym13061080

Katalog-ID:	DOAJ024031917

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520			\|a The wide application of encryption technology has made traffic classification gradually become a major challenge in the field of network security. Traditional methods such as machine learning, which rely heavily on feature engineering and others, can no longer fully meet the needs of encrypted traffic classification. Therefore, we propose an Inception-LSTM(ICLSTM) traffic classification method in this paper to achieve encrypted traffic service identification. This method converts traffic data into common gray images, and then uses the constructed ICLSTM neural network to extract key features and perform effective traffic classification. To alleviate the problem of category imbalance, different weight parameters are set for each category separately in the training phase to make it more symmetrical for different categories of encrypted traffic, and the identification effect is more balanced and reasonable. The method is validated on the public ISCX 2016 dataset, and the results of five classification experiments show that the accuracy of the method exceeds 98% for both regular encrypted traffic service identification and VPN encrypted traffic service identification. At the same time, this deep learning-based classification method also greatly simplifies the difficulty of traffic feature extraction work.
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allfields	10.3390/sym13061080 doi (DE-627)DOAJ024031917 (DE-599)DOAJ5718e7717a434849802177fca4dd2928 DE-627 ger DE-627 rakwb eng QA1-939 Bei Lu verfasserin aut ICLSTM: Encrypted Traffic Service Identification Based on Inception-LSTM Neural Network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The wide application of encryption technology has made traffic classification gradually become a major challenge in the field of network security. Traditional methods such as machine learning, which rely heavily on feature engineering and others, can no longer fully meet the needs of encrypted traffic classification. Therefore, we propose an Inception-LSTM(ICLSTM) traffic classification method in this paper to achieve encrypted traffic service identification. This method converts traffic data into common gray images, and then uses the constructed ICLSTM neural network to extract key features and perform effective traffic classification. To alleviate the problem of category imbalance, different weight parameters are set for each category separately in the training phase to make it more symmetrical for different categories of encrypted traffic, and the identification effect is more balanced and reasonable. The method is validated on the public ISCX 2016 dataset, and the results of five classification experiments show that the accuracy of the method exceeds 98% for both regular encrypted traffic service identification and VPN encrypted traffic service identification. At the same time, this deep learning-based classification method also greatly simplifies the difficulty of traffic feature extraction work. encrypted traffic service identification neural network inception LSTM Mathematics Nurbol Luktarhan verfasserin aut Chao Ding verfasserin aut Wenhui Zhang verfasserin aut In Symmetry MDPI AG, 2009 13(2021), 6, p 1080 (DE-627)610604112 (DE-600)2518382-5 20738994 nnns volume:13 year:2021 number:6, p 1080 https://doi.org/10.3390/sym13061080 kostenfrei https://doaj.org/article/5718e7717a434849802177fca4dd2928 kostenfrei https://www.mdpi.com/2073-8994/13/6/1080 kostenfrei https://doaj.org/toc/2073-8994 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 6, p 1080
spelling	10.3390/sym13061080 doi (DE-627)DOAJ024031917 (DE-599)DOAJ5718e7717a434849802177fca4dd2928 DE-627 ger DE-627 rakwb eng QA1-939 Bei Lu verfasserin aut ICLSTM: Encrypted Traffic Service Identification Based on Inception-LSTM Neural Network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The wide application of encryption technology has made traffic classification gradually become a major challenge in the field of network security. Traditional methods such as machine learning, which rely heavily on feature engineering and others, can no longer fully meet the needs of encrypted traffic classification. Therefore, we propose an Inception-LSTM(ICLSTM) traffic classification method in this paper to achieve encrypted traffic service identification. This method converts traffic data into common gray images, and then uses the constructed ICLSTM neural network to extract key features and perform effective traffic classification. To alleviate the problem of category imbalance, different weight parameters are set for each category separately in the training phase to make it more symmetrical for different categories of encrypted traffic, and the identification effect is more balanced and reasonable. The method is validated on the public ISCX 2016 dataset, and the results of five classification experiments show that the accuracy of the method exceeds 98% for both regular encrypted traffic service identification and VPN encrypted traffic service identification. At the same time, this deep learning-based classification method also greatly simplifies the difficulty of traffic feature extraction work. encrypted traffic service identification neural network inception LSTM Mathematics Nurbol Luktarhan verfasserin aut Chao Ding verfasserin aut Wenhui Zhang verfasserin aut In Symmetry MDPI AG, 2009 13(2021), 6, p 1080 (DE-627)610604112 (DE-600)2518382-5 20738994 nnns volume:13 year:2021 number:6, p 1080 https://doi.org/10.3390/sym13061080 kostenfrei https://doaj.org/article/5718e7717a434849802177fca4dd2928 kostenfrei https://www.mdpi.com/2073-8994/13/6/1080 kostenfrei https://doaj.org/toc/2073-8994 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 6, p 1080
allfields_unstemmed	10.3390/sym13061080 doi (DE-627)DOAJ024031917 (DE-599)DOAJ5718e7717a434849802177fca4dd2928 DE-627 ger DE-627 rakwb eng QA1-939 Bei Lu verfasserin aut ICLSTM: Encrypted Traffic Service Identification Based on Inception-LSTM Neural Network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The wide application of encryption technology has made traffic classification gradually become a major challenge in the field of network security. Traditional methods such as machine learning, which rely heavily on feature engineering and others, can no longer fully meet the needs of encrypted traffic classification. Therefore, we propose an Inception-LSTM(ICLSTM) traffic classification method in this paper to achieve encrypted traffic service identification. This method converts traffic data into common gray images, and then uses the constructed ICLSTM neural network to extract key features and perform effective traffic classification. To alleviate the problem of category imbalance, different weight parameters are set for each category separately in the training phase to make it more symmetrical for different categories of encrypted traffic, and the identification effect is more balanced and reasonable. The method is validated on the public ISCX 2016 dataset, and the results of five classification experiments show that the accuracy of the method exceeds 98% for both regular encrypted traffic service identification and VPN encrypted traffic service identification. At the same time, this deep learning-based classification method also greatly simplifies the difficulty of traffic feature extraction work. encrypted traffic service identification neural network inception LSTM Mathematics Nurbol Luktarhan verfasserin aut Chao Ding verfasserin aut Wenhui Zhang verfasserin aut In Symmetry MDPI AG, 2009 13(2021), 6, p 1080 (DE-627)610604112 (DE-600)2518382-5 20738994 nnns volume:13 year:2021 number:6, p 1080 https://doi.org/10.3390/sym13061080 kostenfrei https://doaj.org/article/5718e7717a434849802177fca4dd2928 kostenfrei https://www.mdpi.com/2073-8994/13/6/1080 kostenfrei https://doaj.org/toc/2073-8994 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 6, p 1080
allfieldsGer	10.3390/sym13061080 doi (DE-627)DOAJ024031917 (DE-599)DOAJ5718e7717a434849802177fca4dd2928 DE-627 ger DE-627 rakwb eng QA1-939 Bei Lu verfasserin aut ICLSTM: Encrypted Traffic Service Identification Based on Inception-LSTM Neural Network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The wide application of encryption technology has made traffic classification gradually become a major challenge in the field of network security. Traditional methods such as machine learning, which rely heavily on feature engineering and others, can no longer fully meet the needs of encrypted traffic classification. Therefore, we propose an Inception-LSTM(ICLSTM) traffic classification method in this paper to achieve encrypted traffic service identification. This method converts traffic data into common gray images, and then uses the constructed ICLSTM neural network to extract key features and perform effective traffic classification. To alleviate the problem of category imbalance, different weight parameters are set for each category separately in the training phase to make it more symmetrical for different categories of encrypted traffic, and the identification effect is more balanced and reasonable. The method is validated on the public ISCX 2016 dataset, and the results of five classification experiments show that the accuracy of the method exceeds 98% for both regular encrypted traffic service identification and VPN encrypted traffic service identification. At the same time, this deep learning-based classification method also greatly simplifies the difficulty of traffic feature extraction work. encrypted traffic service identification neural network inception LSTM Mathematics Nurbol Luktarhan verfasserin aut Chao Ding verfasserin aut Wenhui Zhang verfasserin aut In Symmetry MDPI AG, 2009 13(2021), 6, p 1080 (DE-627)610604112 (DE-600)2518382-5 20738994 nnns volume:13 year:2021 number:6, p 1080 https://doi.org/10.3390/sym13061080 kostenfrei https://doaj.org/article/5718e7717a434849802177fca4dd2928 kostenfrei https://www.mdpi.com/2073-8994/13/6/1080 kostenfrei https://doaj.org/toc/2073-8994 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 6, p 1080
allfieldsSound	10.3390/sym13061080 doi (DE-627)DOAJ024031917 (DE-599)DOAJ5718e7717a434849802177fca4dd2928 DE-627 ger DE-627 rakwb eng QA1-939 Bei Lu verfasserin aut ICLSTM: Encrypted Traffic Service Identification Based on Inception-LSTM Neural Network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The wide application of encryption technology has made traffic classification gradually become a major challenge in the field of network security. Traditional methods such as machine learning, which rely heavily on feature engineering and others, can no longer fully meet the needs of encrypted traffic classification. Therefore, we propose an Inception-LSTM(ICLSTM) traffic classification method in this paper to achieve encrypted traffic service identification. This method converts traffic data into common gray images, and then uses the constructed ICLSTM neural network to extract key features and perform effective traffic classification. To alleviate the problem of category imbalance, different weight parameters are set for each category separately in the training phase to make it more symmetrical for different categories of encrypted traffic, and the identification effect is more balanced and reasonable. The method is validated on the public ISCX 2016 dataset, and the results of five classification experiments show that the accuracy of the method exceeds 98% for both regular encrypted traffic service identification and VPN encrypted traffic service identification. At the same time, this deep learning-based classification method also greatly simplifies the difficulty of traffic feature extraction work. encrypted traffic service identification neural network inception LSTM Mathematics Nurbol Luktarhan verfasserin aut Chao Ding verfasserin aut Wenhui Zhang verfasserin aut In Symmetry MDPI AG, 2009 13(2021), 6, p 1080 (DE-627)610604112 (DE-600)2518382-5 20738994 nnns volume:13 year:2021 number:6, p 1080 https://doi.org/10.3390/sym13061080 kostenfrei https://doaj.org/article/5718e7717a434849802177fca4dd2928 kostenfrei https://www.mdpi.com/2073-8994/13/6/1080 kostenfrei https://doaj.org/toc/2073-8994 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 6, p 1080
language	English
source	In Symmetry 13(2021), 6, p 1080 volume:13 year:2021 number:6, p 1080
sourceStr	In Symmetry 13(2021), 6, p 1080 volume:13 year:2021 number:6, p 1080
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	encrypted traffic service identification neural network inception LSTM Mathematics
isfreeaccess_bool	true
container_title	Symmetry
authorswithroles_txt_mv	Bei Lu @@aut@@ Nurbol Luktarhan @@aut@@ Chao Ding @@aut@@ Wenhui Zhang @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
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Traditional methods such as machine learning, which rely heavily on feature engineering and others, can no longer fully meet the needs of encrypted traffic classification. Therefore, we propose an Inception-LSTM(ICLSTM) traffic classification method in this paper to achieve encrypted traffic service identification. This method converts traffic data into common gray images, and then uses the constructed ICLSTM neural network to extract key features and perform effective traffic classification. To alleviate the problem of category imbalance, different weight parameters are set for each category separately in the training phase to make it more symmetrical for different categories of encrypted traffic, and the identification effect is more balanced and reasonable. 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callnumber-first	Q - Science
author	Bei Lu
spellingShingle	Bei Lu misc QA1-939 misc encrypted traffic service identification misc neural network misc inception misc LSTM misc Mathematics ICLSTM: Encrypted Traffic Service Identification Based on Inception-LSTM Neural Network
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topic_title	QA1-939 ICLSTM: Encrypted Traffic Service Identification Based on Inception-LSTM Neural Network encrypted traffic service identification neural network inception LSTM
topic	misc QA1-939 misc encrypted traffic service identification misc neural network misc inception misc LSTM misc Mathematics
topic_unstemmed	misc QA1-939 misc encrypted traffic service identification misc neural network misc inception misc LSTM misc Mathematics
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title	ICLSTM: Encrypted Traffic Service Identification Based on Inception-LSTM Neural Network
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title_full	ICLSTM: Encrypted Traffic Service Identification Based on Inception-LSTM Neural Network
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title_auth	ICLSTM: Encrypted Traffic Service Identification Based on Inception-LSTM Neural Network
abstract	The wide application of encryption technology has made traffic classification gradually become a major challenge in the field of network security. Traditional methods such as machine learning, which rely heavily on feature engineering and others, can no longer fully meet the needs of encrypted traffic classification. Therefore, we propose an Inception-LSTM(ICLSTM) traffic classification method in this paper to achieve encrypted traffic service identification. This method converts traffic data into common gray images, and then uses the constructed ICLSTM neural network to extract key features and perform effective traffic classification. To alleviate the problem of category imbalance, different weight parameters are set for each category separately in the training phase to make it more symmetrical for different categories of encrypted traffic, and the identification effect is more balanced and reasonable. The method is validated on the public ISCX 2016 dataset, and the results of five classification experiments show that the accuracy of the method exceeds 98% for both regular encrypted traffic service identification and VPN encrypted traffic service identification. At the same time, this deep learning-based classification method also greatly simplifies the difficulty of traffic feature extraction work.
abstractGer	The wide application of encryption technology has made traffic classification gradually become a major challenge in the field of network security. Traditional methods such as machine learning, which rely heavily on feature engineering and others, can no longer fully meet the needs of encrypted traffic classification. Therefore, we propose an Inception-LSTM(ICLSTM) traffic classification method in this paper to achieve encrypted traffic service identification. This method converts traffic data into common gray images, and then uses the constructed ICLSTM neural network to extract key features and perform effective traffic classification. To alleviate the problem of category imbalance, different weight parameters are set for each category separately in the training phase to make it more symmetrical for different categories of encrypted traffic, and the identification effect is more balanced and reasonable. The method is validated on the public ISCX 2016 dataset, and the results of five classification experiments show that the accuracy of the method exceeds 98% for both regular encrypted traffic service identification and VPN encrypted traffic service identification. At the same time, this deep learning-based classification method also greatly simplifies the difficulty of traffic feature extraction work.
abstract_unstemmed	The wide application of encryption technology has made traffic classification gradually become a major challenge in the field of network security. Traditional methods such as machine learning, which rely heavily on feature engineering and others, can no longer fully meet the needs of encrypted traffic classification. Therefore, we propose an Inception-LSTM(ICLSTM) traffic classification method in this paper to achieve encrypted traffic service identification. This method converts traffic data into common gray images, and then uses the constructed ICLSTM neural network to extract key features and perform effective traffic classification. To alleviate the problem of category imbalance, different weight parameters are set for each category separately in the training phase to make it more symmetrical for different categories of encrypted traffic, and the identification effect is more balanced and reasonable. The method is validated on the public ISCX 2016 dataset, and the results of five classification experiments show that the accuracy of the method exceeds 98% for both regular encrypted traffic service identification and VPN encrypted traffic service identification. At the same time, this deep learning-based classification method also greatly simplifies the difficulty of traffic feature extraction work.
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title_short	ICLSTM: Encrypted Traffic Service Identification Based on Inception-LSTM Neural Network
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