Study on privacy preserving encrypted traffic detection
Existing encrypted traffic detection technologies lack privacy protection for data and models, which will violate the privacy preserving regulations and increase the security risk of privacy leakage.A privacy-preserving encrypted traffic detection system was proposed.It promoted the privacy of the e...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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POSTS&TELECOM PRESS Co., LTD
2021-08-01
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| Series: | 网络与信息安全学报 |
| Subjects: | |
| Online Access: | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2021057 |
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| _version_ | 1841529873383817216 |
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| author | Xinyu ZHANG Bingsheng ZHANG Quanrun MENG Kui REN |
| author_facet | Xinyu ZHANG Bingsheng ZHANG Quanrun MENG Kui REN |
| author_sort | Xinyu ZHANG |
| collection | DOAJ |
| description | Existing encrypted traffic detection technologies lack privacy protection for data and models, which will violate the privacy preserving regulations and increase the security risk of privacy leakage.A privacy-preserving encrypted traffic detection system was proposed.It promoted the privacy of the encrypted traffic detection model by combining the gradient boosting decision tree (GBDT) algorithm with differential privacy.The privacy-protected encrypted traffic detection system was designed and implemented.The performance and the efficiency of proposed system using the CICIDS2017 dataset were evaluated, which contained the malicious traffic of the DDoS attack and the port scan.The results show that when the privacy budget value is set to 1, the system accuracy rates are 91.7% and 92.4% respectively.The training and the prediction of our model is efficient.The training time of proposed model is 5.16 s and 5.59 s, that is only 2-3 times of GBDT algorithm.The prediction time is close to the GBDT algorithm. |
| format | Article |
| id | doaj-art-43ac420bc4ec411ebff8dc6961354cb2 |
| institution | Kabale University |
| issn | 2096-109X |
| language | English |
| publishDate | 2021-08-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 网络与信息安全学报 |
| spelling | doaj-art-43ac420bc4ec411ebff8dc6961354cb22025-01-15T03:15:05ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2021-08-01710111359567852Study on privacy preserving encrypted traffic detectionXinyu ZHANGBingsheng ZHANGQuanrun MENGKui RENExisting encrypted traffic detection technologies lack privacy protection for data and models, which will violate the privacy preserving regulations and increase the security risk of privacy leakage.A privacy-preserving encrypted traffic detection system was proposed.It promoted the privacy of the encrypted traffic detection model by combining the gradient boosting decision tree (GBDT) algorithm with differential privacy.The privacy-protected encrypted traffic detection system was designed and implemented.The performance and the efficiency of proposed system using the CICIDS2017 dataset were evaluated, which contained the malicious traffic of the DDoS attack and the port scan.The results show that when the privacy budget value is set to 1, the system accuracy rates are 91.7% and 92.4% respectively.The training and the prediction of our model is efficient.The training time of proposed model is 5.16 s and 5.59 s, that is only 2-3 times of GBDT algorithm.The prediction time is close to the GBDT algorithm.http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2021057privacy-preservingencrypted traffic detectiongradient boosting decision treedifferential privacy |
| spellingShingle | Xinyu ZHANG Bingsheng ZHANG Quanrun MENG Kui REN Study on privacy preserving encrypted traffic detection 网络与信息安全学报 privacy-preserving encrypted traffic detection gradient boosting decision tree differential privacy |
| title | Study on privacy preserving encrypted traffic detection |
| title_full | Study on privacy preserving encrypted traffic detection |
| title_fullStr | Study on privacy preserving encrypted traffic detection |
| title_full_unstemmed | Study on privacy preserving encrypted traffic detection |
| title_short | Study on privacy preserving encrypted traffic detection |
| title_sort | study on privacy preserving encrypted traffic detection |
| topic | privacy-preserving encrypted traffic detection gradient boosting decision tree differential privacy |
| url | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2021057 |
| work_keys_str_mv | AT xinyuzhang studyonprivacypreservingencryptedtrafficdetection AT bingshengzhang studyonprivacypreservingencryptedtrafficdetection AT quanrunmeng studyonprivacypreservingencryptedtrafficdetection AT kuiren studyonprivacypreservingencryptedtrafficdetection |