[1] |
HERRMANN D , WENDOLSKY R , FEDERRATH H . Website fingerprinting:attacking popular privacy enhancing technologies with the multinomial na?ve-Bayes classifier[C]// Proceedings of the 2009 ACM Workshop on Cloud Computing Security. New York:ACM Press, 2009: 31-42.
|
[2] |
PANCHENKO A , NIESSEN L , ZINNEN A ,et al. Website fingerprinting in onion routing based anonymization networks[C]// Proceedings of the 10th Annual ACM Workshop on Privacy in the Electronic Society. New York:ACM Press, 2011: 103-114.
|
[3] |
WANG T , CAI X , NITHYANAND R ,et al. Effective attacks and provable defenses for website fingerprinting[C]// 23rd USENIX Security Symposium. Berkeley:USENIX Association, 2014: 143-157.
|
[4] |
SCOTT-HAYWARD S , O’CALLAGHAN G , SEZER S . SDN security:a survey[C]// Proceedings of 2013 IEEE SDN for Future Networks and Services. Piscataway:IEEE Press, 2013: 1-7.
|
[5] |
魏松杰, 孙鑫, 赵茹东 ,等. SDN中IP欺骗数据分组网络溯源方法研究[J]. 通信学报, 2018,39(11): 181-189.
|
|
WEI S J , SUN X , ZHAO R D ,et al. Tracing IP-spoofed packets in software defined network[J]. Journal on Communications, 2018,39(11): 181-189.
|
[6] |
SONG H O , XIANG Y , JEGELKA S ,et al. Deep metric learning via lifted structured feature embedding[C]// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016: 4004-4012.
|
[7] |
祝现威, 常朝稳, 朱智强 ,等. 基于身份属性的SDN控制转发方法[J]. 通信学报, 2019,40(11): 1-18.
|
|
ZHU X W , CHANG C W , ZHU Z Q ,et al. SDN control and forwarding method based on identity attribute[J]. Journal on Communications, 2019,40(11): 1-18.
|
[8] |
BENZEKKI K , FERGOUGUI A E , ELALAOUI A E . Software-defined networking (SDN):a survey[J]. Security and Communication Networks, 2016,9(18): 5803-5833.
|
[9] |
OCONNOR T , ENCK W , PETULLO W M ,et al. PivotWall:SDN-based information flow control[C]// Proceedings of the Symposium on SDN Research.[S.l.:s.n.], 2018: 1-14.
|
[10] |
LING Z , LUO J Z , XU D N ,et al. Novel and practical SDN-based traceback technique for malicious traffic over anonymous networks[C]// Proceedings of IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. Piscataway:IEEE Press, 2019: 1180-1188.
|
[11] |
MOORE A W , PAPAGIANNAKI K . Toward the accurate identification of network applications[C]// International Workshop on Passive and Active Network Measurement,Berlin:Springer, 2005: 41-54.
|
[12] |
FINSTERBUSCH M , RICHTER C , ROCHA E ,et al. A survey of payload-based traffic classification approaches[J]. IEEE Communications Surveys & Tutorials, 2014,16(2): 1135-1156.
|
[13] |
WANG T , GOLDBERG I . Improved website fingerprinting on tor[C]// Proceedings of the 12th ACM Workshop on Privacy in the Electronic Society. New York:ACM Press, 2013: 201-212.
|
[14] |
CAI X , ZHANG X C , JOSHI B ,et al. Touching from a distance:website fingerprinting attacks and defenses[C]// Proceedings of the 2012 ACM Conference on Computer and Communications Security. New York:ACM Press, 2012: 605-616.
|
[15] |
HE K M , ZHANG X Y , REN S Q ,et al. Deep residual learning for image recognition[C]// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016: 770-778.
|
[16] |
ZHU J Y , PARK T , ISOLA P ,et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]// Proceedings of 2017 IEEE International Conference on Computer Vision. Piscataway:IEEE Press, 2017: 2242-2251.
|
[17] |
SIRINAM P , IMANI M , JUAREZ M ,et al. Deep fingerprinting:undermining website fingerprinting defenses with deep learning[C]// Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. New York:ACM Press, 2018: 1928-1943.
|
[18] |
RIMMER V , PREUVENEERS D , JUAREZ M ,et al. Automated website fingerprinting through deep learning[C]// Proceedings of 2018 Network and Distributed System Security Symposium. Reston:Internet Society, 2018: 1-16.
|
[19] |
BHAT S , LU D , KWON A ,et al. Var-CNN:a data-efficient website fingerprinting attack based on deep learning[J]. Proceedings on Privacy Enhancing Technologies, 2019,2019(4): 292-310.
|
[20] |
SHEN M , LIU Y T , ZHU L H ,et al. Fine-grained webpage fingerprinting using only packet length information of encrypted traffic[J]. IEEE Transactions on Information Forensics and Security, 2021,16: 2046-2059.
|
[21] |
CADENA W D L , MITSEVA A , HILLER J ,et al. TrafficSliver:fighting website fingerprinting attacks with traffic splitting[C]// Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security. New York:ACM Press, 2020: 1971-1985.
|
[22] |
HARDEGEN C , PFüLB B , RIEGER S ,et al. Predicting network flow characteristics using deep learning and real-world network traffic[J]. IEEE Transactions on Network and Service Management, 2020,17(4): 2662-2676.
|
[23] |
SHI Y , MATSUURA K . Fingerprinting attack on the tor anonymity system[C]// International Conference on Information and Communications Security. Berlin:Springer, 2009: 425-438.
|
[24] |
PANCHENKO A , LANZE F B , ZINNEN A ,et al. Website fingerprinting at Internet scale[C]// Proceedings of 2016 Network and Distributed System Security Symposium. Reston:Internet Society, 2016: 1-15.
|
[25] |
HAYES J , DANEZIS G . K-fingerprinting:a robust scalable website fingerprinting technique[J]. arXiv Preprint,arXiv:1509.00789, 2015.
|
[26] |
MATIC S , TRONCOSO C , CABALLERO J . Dissecting tor bridges:a security evaluation of their private and public infrastructures[C]// Proceedings of 2017 Network and Distributed System Security Symposium. Reston:Internet Society, 2017: 1-15.
|
[27] |
LING Z , LUO J Z , YU W ,et al. Extensive analysis and large-scale empirical evaluation of tor bridge discovery[C]// 2012 Proceedings of IEEE INFOCOM. Piscataway:IEEE Press, 2012: 2381-2389.
|
[28] |
KINGMA D P , BA J . Adam:a method for stochastic optimization[J]. arXiv Preprint,arXiv:1412.6980, 2014.
|
[29] |
WANG L M , MEI H T , SHENG V S . Multilevel identification and classification analysis of tor on mobile and PC platforms[J]. IEEE Transactions on Industrial Informatics, 2021,17(2): 1079-1088.
|
[30] |
LOTFOLLAHI M , SIAVOSHANI M J , ZADE R S H ,et al. Deep packet:a novel approach for encrypted traffic classification using deep learning[J]. Soft Computing, 2020,24(3): 1999-2012.
|
[31] |
CHOPRA S , HADSELL R , LECUN Y . Learning a similarity metric discriminatively,with application to face verification[C]// Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2005: 539-546.
|
[32] |
SCHROFF F , KALENICHENKO D , PHILBIN J . FaceNet:a unified embedding for face recognition and clustering[C]// Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2015: 815-823.
|
[33] |
SOHN K , . Improved deep metric learning with multiclass n-pair loss objective[C]// Advances in Neural Information Processing Systems.[S.l.:s.n.], 2016: 1857-1865.
|
[34] |
PEREZ L , WANG J . The effectiveness of data augmentation in image classification using deep learning[J]. arXiv Preprint,arXiv:1712.04621, 2017.
|
[35] |
兰巨龙, 张学帅, 胡宇翔 ,等. 基于深度强化学习的软件定义网络QoS优化[J]. 通信学报, 2019,40(12): 60-67.
|
|
LAN J L , ZHANG X S , HU Y X ,et al. Software-defined networking QoS optimization based on deep reinforcement learning[J]. Journal on Communications, 2019,40(12): 60-67.
|
[36] |
SUTSKEVER I , MARTENS J , DAHL G ,et al. On the importance of initialization and momentum in deep learning[C]// International Conference on Machine Learning.[S.l.:s.n.], 2013: 1139-1147.
|
[37] |
ZEILER M D . ADADELTA:an adaptive learning rate method[J]. arXiv Preprint,arXiv:1212.5701, 2012.
|