Research on Encrypted Traffic Detection Based on Key Features
Most of the traffic on the Internet is encrypted traffic, and the detection of encrypted traffic is the current difficulty, because the internal features of the data are destroyed after encryption, and it is difficult to detect. Most of the existing detection of encrypted traffic is based on the ext...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10375493/ |
| _version_ | 1797361424341663744 |
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| author | Fangjie Chen Jingpeng Bai Weihan Gao |
| author_facet | Fangjie Chen Jingpeng Bai Weihan Gao |
| author_sort | Fangjie Chen |
| collection | DOAJ |
| description | Most of the traffic on the Internet is encrypted traffic, and the detection of encrypted traffic is the current difficulty, because the internal features of the data are destroyed after encryption, and it is difficult to detect. Most of the existing detection of encrypted traffic is based on the external features of encrypted traffic, which requires the extraction of full-cycle information of traffic, and has poor real-time performance. Therefore, based on the internal features of encrypted traffic, this paper proposes a Key Feature Fusion Detection (KFFD) method based on generative adversarial network, which restores the destroyed internal features by encryption key and generative adversarial network, and then improves the internal feature recognition effect of encrypted traffic. Experiments using the Kaggle dataset show that the KFFD method can improve the detection performance of encrypted traffic to a certain extent. |
| first_indexed | 2024-03-08T15:53:30Z |
| format | Article |
| id | doaj.art-4217e1b3fe93447aa0fd7b93f05a38c3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T15:53:30Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4217e1b3fe93447aa0fd7b93f05a38c32024-01-09T00:04:59ZengIEEEIEEE Access2169-35362024-01-01121786179310.1109/ACCESS.2023.334780610375493Research on Encrypted Traffic Detection Based on Key FeaturesFangjie Chen0https://orcid.org/0009-0005-7608-951XJingpeng Bai1Weihan Gao2China Telecom Research Institute, Guangzhou, Tianhe, ChinaChina Telecom Research Institute, Guangzhou, Tianhe, ChinaChina Telecom Research Institute, Guangzhou, Tianhe, ChinaMost of the traffic on the Internet is encrypted traffic, and the detection of encrypted traffic is the current difficulty, because the internal features of the data are destroyed after encryption, and it is difficult to detect. Most of the existing detection of encrypted traffic is based on the external features of encrypted traffic, which requires the extraction of full-cycle information of traffic, and has poor real-time performance. Therefore, based on the internal features of encrypted traffic, this paper proposes a Key Feature Fusion Detection (KFFD) method based on generative adversarial network, which restores the destroyed internal features by encryption key and generative adversarial network, and then improves the internal feature recognition effect of encrypted traffic. Experiments using the Kaggle dataset show that the KFFD method can improve the detection performance of encrypted traffic to a certain extent.https://ieeexplore.ieee.org/document/10375493/Network securityintrusion detection systemsencrypted traffic detectionGANkey featuresartificial intelligence |
| spellingShingle | Fangjie Chen Jingpeng Bai Weihan Gao Research on Encrypted Traffic Detection Based on Key Features IEEE Access Network security intrusion detection systems encrypted traffic detection GAN key features artificial intelligence |
| title | Research on Encrypted Traffic Detection Based on Key Features |
| title_full | Research on Encrypted Traffic Detection Based on Key Features |
| title_fullStr | Research on Encrypted Traffic Detection Based on Key Features |
| title_full_unstemmed | Research on Encrypted Traffic Detection Based on Key Features |
| title_short | Research on Encrypted Traffic Detection Based on Key Features |
| title_sort | research on encrypted traffic detection based on key features |
| topic | Network security intrusion detection systems encrypted traffic detection GAN key features artificial intelligence |
| url | https://ieeexplore.ieee.org/document/10375493/ |
| work_keys_str_mv | AT fangjiechen researchonencryptedtrafficdetectionbasedonkeyfeatures AT jingpengbai researchonencryptedtrafficdetectionbasedonkeyfeatures AT weihangao researchonencryptedtrafficdetectionbasedonkeyfeatures |