Study on privacy preserving encrypted traffic detection

Xinyu ZHANG; Bingsheng ZHANG; Quanrun MENG; Kui REN

doi:10.11959/j.issn.2096-109x.2021057

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Study on privacy preserving encrypted traffic detection

TopicⅡ: Technology and Application of Cryptology | 更新时间：2024-06-03

- Study on privacy preserving encrypted traffic detection
- Chinese Journal of Network and Information Security Vol. 7, Issue 4, Pages: 101-113(2021)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2021057
  CLC： TP393
- Online First：2021-08，
  
  Published：15 August 2021
- 稿件说明：
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Xinyu ZHANG, Bingsheng ZHANG, Quanrun MENG, et al. Study on privacy preserving encrypted traffic detection[J]. Chinese Journal of Network and Information Security, 2021, 7(4): 101-113.
DOI：

Xinyu ZHANG, Bingsheng ZHANG, Quanrun MENG, et al. Study on privacy preserving encrypted traffic detection[J]. Chinese Journal of Network and Information Security, 2021, 7(4): 101-113. DOI： 10.11959/j.issn.2096-109x.2021057.

摘要

现有的加密流量检测技术缺少对数据和模型的隐私性保护，不仅违反了隐私保护法律法规，而且会导致严重的敏感信息泄露。主要研究了基于梯度提升决策树（GBDT）算法的加密流量检测模型，结合差分隐私技术，设计并实现了一个隐私保护的加密流量检测系统。在CICIDS2017数据集下检测了DDoS攻击和端口扫描的恶意流量，并对系统性能进行测试。实验结果表明，当隐私预算ε取值为1时，两个数据集下流量识别准确率分别为91.7%和92.4%，并且模型的训练效率、预测效率较高，训练时间为5.16 s和5.59 s，仅是GBDT算法的2～3倍，预测时间与GBDT算法的预测时间相近，达到了系统安全性和可用性的平衡。

Abstract

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.

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references

MC-GREW D , ANDERSON B . Enhanced telemetry for encrypted threat analytics [C ] // 2016 IEEE 24th International Conference on Network Protocols (ICNP) . 2016 : 1 - 6 .

VOIGT P , VON DEM BUSSCHE A . Practical implementation of the requirements under the GDPR [M ] // The EU General Data Protection Regulation (GDPR) . Cham : Springer International Publishing , 2017 : 245 - 249 .

BUKATY P . The CCPA [M ] // The California Consumer Privacy Act (CCPA) . IT Governance Publishing , 2019 : 123 - 169 .

王春晖 . 《网络安全法》六大法律制度解析 [J ] . 南京邮电大学学报:自然科学版 , 2017 , 37 ( 1 ): 1 - 13 .

WANG C H . Analysis of the six legal systems of Network Security Law [J ] . Journal of Nanjing University of Posts and Telecommuni-cations:Natural Science Edition , 2017 , 37 ( 1 ): 1 - 13 .

洪延青 , 葛鑫 . 国家标准《信息安全技术个人信息安全规范》修订解读 [J ] . 保密科学技术 , 2019 ( 6 ): 6 .

HONG Y Q , GE X . Information Security Technology Personal In-formation Security Specification revision interpretation [J ] . Security Science and Technology , 2019 ( 6 ): 6 .

诸葛建伟 , 韩心慧 , 周勇林 , 等 . 僵尸网络研究 [J ] . 软件学报 , 2008 , 19 ( 3 ): 702 - 715 .

ZHUGE J W , HAN X H , ZHOU Y L , et al . Research and develop-ment of Botnets [J ] . Journal of Software , 2008 , 19 ( 3 ): 702 - 715 .

CAO Z , XIONG G , ZHAO Y , et al . A survey on encrypted trafficclassification [M ] // Applications and Techniques in Information Security . Springer Berlin Heidelberg , 2014 : 73 - 81 .

KLEINBAUM D G , DIETZ K , GAIL M , et al . Logistic regression [M ] . Springer , 2002 .

HO T K , . Random decision forests [C ] // Proceedings of 3rd International Conference on Document Analysis and Recognition:Volume 1 . 1995 : 278 - 282 .

FANG Y , QIU Y , LIU L , et al . Detecting webshell based on random forest with fasttext [C ] // Proceedings of the 2018International Conference on Computing and Artificial Intelligence . 2018 : 52 - 56 .

SI S , ZHANG H , KEERTHI S S , et al . Gradient boosted decision trees for high dimensional sparse output [C ] // Proceedings of the 34th International Conference on Machine Learning-Volume 70 (ICML’17) . 2017 : 3182 - 3190 .

翟征德 , 宗兆伟 . 针对DNS服务器的抗DDoS安全网关系统：中国,CN101572701B [P ] . 2013 - 11 - 20 .

ZHAI Z D , ZONG Z W . Anti-DDoS security gateway system for DNS server [P ] . 2013 - 11 - 20 .

SHOKRI R , STRONATI M , SONG C , et al . Membership inference attacks against machine learning models [C ] // 2017 IEEE Symposium on Security and Privacy (SP) . 2017 : 3 - 18 .

SWEENEY L . K-anonymity:a model for protecting privacy [J ] . International Journal of Uncertainty,Fuzziness and Knowledge-Based Systems , 2002 , 10 ( 5 ): 557 - 570 .

MACHANAVAJJHALA A , KIFER D , GEHRKE J , et al . l-diversity:privacy beyond k-anonymity [J ] . ACM Transactions on Knowledge Discovery from Data (TKDD) , 2007 , 1 ( 1 ): 3 .

DWORK C , . Differential privacy:a survey of results [C ] // International Conference on Theory and Applications of Models of Computation . 2008 : 1 - 19 .

FRANK M , MIRONOV I . Differentially private recommender systems:building privacy into the net [C ] // ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2009 : 627 - 636 .

LI Q , WU Z , WEN Z , et al . Privacy-preserving gradient boosting decision trees [C ] // Proceedings of the AAAI Conference on Artificial Intelligence . 2020 : 784 - 791 .

KE G L , MENG Q , FINLEY T , et al . LightGBM:a highly efficient gradient boosting decision tree [C ] // Advances in Neural Information Processing Systems 30 (NIPS 2017) . 2017 : 3149 - 3157 .

SHARAFALDIN I , LASHKARI A H , GHORBANI A A . Toward Generating a new intrusion detection dataset and intrusion traffic characterization [C ] // 4th International Conference on Information Systems Security and Privacy (ICISSP) . 2018 .

LIU Z , WU Z , LI T , et al . GMM and CNN hybrid method for short utterance speaker recognition [J ] . IEEE Transactions on Industrial Informatics , 2018 , 14 ( 7 ): 3244 - 3252 .

KRIZHEVSKY A , SUTSKEVER I , HINTON G E . Imagenet classification with deep convolutional neural networks [J ] . Advances in Communications of the ACM , 2012 : 1097 - 1105 .

MEIDAN Y , BOHADANA M , SHABTAI A , et al . ProfilIoT:a machine learning approach for IoT device identification based on network traffic analysis [C ] // Proceedings of the Symposium on Applied Computing . 2017 : 506 - 509 .

CHEN T , ZHONG S . Privacy-preserving backpropagation neural network learning [J ] . IEEE Transactions on Neural Networks , 2009 , 20 ( 10 ): 1554 - 1564 .

ERKIN Z , VEUGEN T ,, TOFT T , LAGENDIJK R L . Generating private recommendations efficiently using homomorphic encryption and data packing [J ] . IEEE Trans Inf Forensics Security , 2012 , 7 ( 3 ): 1053 - 1066 .

BOST R , POPA R ,, TU S , GOLDWASSER S . Machine learning classification over encrypted data [C ] // NDSS . 2015 :4325.

GILAD-BACHRACH R , DOWLIN N , LAINE K , et al . Cryptonets:applying neural networks to encrypted data with high throughput and accuracy [C ] // International Conference on Machine Learning . 2016 : 201 - 210 .

HESAMIFARD E , TAKABI H , GHASEMI M . CryptoDL:deep neural networks over encrypted data [J ] . ArXiv preprint ArXiv:1711.05189 , 2017 .

MOHASSEL P , ZHANG Y . SecureML:a system for scalable privacy-preserving machine learning [C ] // 2017 IEEE Symposium on Security and Privacy (SP) . 2017 : 19 - 38 .

KONEČNÝ I , MCMAHAN H B , YU F X , et al . Federated learning:Strategies for improving communication efficiency [J ] . arXiv preprint arXiv:1610.05492 , 2016 .

MOHASSEL P , RINDAL P . ABY3:a mixed protocol framework for machine learning [C ] // Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security . 2018 : 35 - 52 .

PATRA A , SURESH A . BLAZE:Blazing Fast Privacy-Preserving Machine Learning [J ] . arXiv preprint arXiv:2005.09042 , 2020 .

DWORK C , TALWAR K , THAKURTA A , et al . Analyze gauss:optimal bounds for privacy-preserving principal component analysis [C ] // Proceedings of the Forty-sixth Annual ACM Symposium on Theory of Computing . 2014 : 11 - 20 .

HEIKKILÄ M , LAGERSPETZ E , KASKI S , et al . Differentially private Bayesian learning on distributed data [C ] // Advances in Neural Information Processing Systems . 2017 : 3226 - 3235 .

HARDT M , PRICE E . The noisy power method:a meta algorithm with applications [C ] // Advances in Neural Information Processing Systems . 2014 : 2861 - 2869 .

ABADI M , CHU A , GOODFELLOW I , et al . Deep learning with differential privacy [C ] // Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security . 2016 : 308 - 318 .

CHAUDHURI K , MONTELEONI C . Privacy-preserving logistic regression [C ] // Advances in Neural Information Processing Systems . 2009 : 289 - 296 .

CHAUDHURI K , SAR WATE D A , SINHA K . A near-optimal algorithm for differentially-private principal components [J ] . Journal of Machine Learning Research , 2013 , 14 ( 1 ): 2905 - 2943 .

CHAUDHURI K , MONTELEONI C , SARWATE A D . Differentially private empirical risk minimization [J ] . Journal of Machine Learning Research , 2011 , 12 : 1069 - 1109 .

ZHANG J , ZHANG Z , XIAO X , et al . Functional mechanism:regression analysis under differential privacy [J ] . arXiv preprint arXiv:1208.0219 , 2012 .

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