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Adaptive Fingerprinting: Website Fingerprinting over Few Encrypted Traffic

Published: 26 April 2021 Publication History

Abstract

Website fingerprinting attacks can infer which website a user visits over encrypted network traffic. Recent studies can achieve high accuracy (e.g., 98%) by leveraging deep neural networks. However, current attacks rely on enormous encrypted traffic data, which are time-consuming to collect. Moreover, large-scale encrypted traffic data also need to be recollected frequently to adjust the changes in the website content. In other words, the bootstrap time for carrying out website fingerprinting is not practical. In this paper, we propose a new method, named Adaptive Fingerprinting, which can derive high attack accuracy over few encrypted traffic by leveraging adversarial domain adaption. With our method, an attacker only needs to collect few traffic rather than large-scale datasets, which makes website fingerprinting more practical in the real world. Our extensive experimental results over multiple datasets show that our method can achieve 89% accuracy over few encrypted traffic in the closed-world setting and 99% precision and 99% recall in the open-world setting. Compared to a recent study (named Triplet Fingerprinting), our method is much more efficient in pre-training time and is more scalable. Moreover, the attack performance of our method can outperform Triplet Fingerprinting in both the closed-world evaluation and open-world evaluation.

Supplementary Material

MP4 File (codaspy21_v2_0308.mp4)
In this study, we propose a new method, named Adaptive Fingerprinting, which can derive high attack accuracy over few encrypted traffic by leveraging adversarial domain adaptation. With our method, an attacker only needs to collect few traffic rather than large-scale datasets, which makes website fingerprinting more practical in the real world. Our experimental results over multiple datasets show that our method can achieve 89% accuracy over few encrypted traffic in the closed-world setting and 99% precision and 99% recall in the open-world setting. Compared to a recent study (named Triplet Fingerprinting), our method has better performance and is more efficient in pre-training time.

References

[1]
2016. WTF-PAD. https://github.com/wtfpad/wtfpad
[2]
2018. tor-browser-crawler. https://github.com/onionpop/tor-browser-crawler
[3]
2021. AdaptiveFingerprinting. https://github.com/SmartHomePrivacyProject/ AdaptiveFingerprinting
[4]
K. Abe and S. Goto. 2016. Fingerprinting Attack on Tor Anonymity Using Deep Learning. In Proc. of Aisa Pacific Advanced Network (APAN).
[5]
S. Bhat, D. Lu, A. Kwon, and S. Devadas. 2019. Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning. In Proc. of PETS'19.
[6]
W. D. Cadena, A. Mitseva, J. Hiller, J. Penekamp, S. Reuter, J. Filter, T. Engel, K. Wehrle, and A. Panchenko. 2020. TrafficSliver: Finghting Website Fingerprinting Attacks with Tra"c Splitting. In Proc. of ACM CCS'20.
[7]
X. Cai, R. Nithyanand, and R. Johnson. 2014. CS-BuFLO: A Congrestion Sensitive Website Fingerprinting Defense. In Proc. of 13th ACM Workshop on Privacy in Electronic Society.
[8]
W. Cui, T. Chen, C. Fields, J. Chen, A. Sierra, and E. Chan-Tin. 2019. Revisting Assumtions for Website Fingerprinting Attacks. In Proc. of ACM ASIACCS'19.
[9]
Kevin P. Dyer, Scott E. Coull, Thomas Ristenpart, and Thomas Shrimpton. 2012. Peek-a-Boo, I Still See You: Why Efficient Traffic Analysis Countermeasures Fail. In Proc. of IEEE S&P'12.
[10]
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky. 2016. Domain-Adversarial Tranining of Neural Networks. Journal of Machine Learning Research (2016).
[11]
Jiajun Gong and Tao Wang. 2020. Zero-delay Lightweight Defenses against Website Fingerprinting. In Proc. of USENIX Security'20.
[12]
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. 2014. Generative Adversarial Nets. In Proc. of the International Conference on Nerual Information Processing Systems (NIPS 2014).
[13]
J. Hayes and G. Danezis. 2016. K-Fingerprinting: A Robust Scalable Website Fingerprinting Technique. In Proc. of USENIX Security'16.
[14]
S. Henri, G. Garcia-Aviles, P. Serrano, A. Banchs, and P. Thiran. 2020. Protecting against Website Fingerprinting with Multihoming. In Proc. of PETS'20.
[15]
Dominik Hermann, Rolf Wendolsky, and Hannes Federrath. 2009. Website Fingertinging: Attacking Popular Privacy Enhancing Tehnologies with the Multinomial Naive-Bayes Classifier. In Proc. of ACM Workshop on Cloud Computing Security.
[16]
D. Ho, E. Liang, I. Stoica, P. Abbeel, and X. Chen. 2019. Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules. In Proc. of ICML'19.
[17]
M. Imani, M. S. Rahman, N. Mathews, and M. Wright. 2019. Mockingbird: Defending Against Deep-Learning-Based Website Fingerprinting Attacks with Adversarial Traces. (2019). https://arxiv.org/pdf/1902.06626.pdf.
[18]
R. Jansen, M. Juarez, R. Galvez, T. Elahi, and C. Diaz. 2018. Inside Job: Applying Traffic Analysis to Measure Tor from Within. In Proc. of NDSS'18.
[19]
M. Juarez, S. Afroz, G. Acar, C. Diaz, and R. Greenstadt. 2014. A Criticial Evaluation of Website Fingerprinting Attacks. In Proc. of ACM CCS'14.
[20]
M. Juarez, M. Imani, M. Perry, C. Diaz, and M. Wright. 2016. Toward an Efficient Website Fingerprinting Defense. In Proc. of ESORICS'16.
[21]
G. Koch, R. Zemel, and R. Salakhutdinov. 2015. Siamese Neural Networks for One-shot Image Recognition. In Proc. of the 32th International Conference on Machine Learning (ICML'15).
[22]
Shuai Li, Huajun Guo, and Nicholas Hopper. 2018. Measuring Information Leakage in Website Fingerprinting Attacks and Defenses. In Proc. of ACM CCS'18.
[23]
Marc Liberatore and Brian Neil Levine. 2006. Inferring the Source of Encrypted HTTP Connections. In Proc. of ACM CCS'06.
[24]
M. Long and J. Wang. 2015. Learning transferable features with deep adaptation networks. In Proc. of ICML'15.
[25]
Laurens van der Maaten and Geo#rey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research 9, Nov (2008), 2579--2605.
[26]
M. Nasr, A. Bahramali, and A. Houmansadr. 2020. Blind Adversarial Network Perturbations. (2020). https://arxiv.org/pdf/2002.06495.pdf.
[27]
Se Eun Oh, S. Sunkam, and N. Hopper. 2019. p-FP: Extraction, Classification, and Predication of Website Fingerprints. In Proc. of PETS'19.
[28]
S. J. Pan and Q. Yang. 2009. A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering (2009).
[29]
A. Panchenko, F. Lanze, A. Zinnen, M. Henze, J. Penekamp, K. Wehrle, and T. Engel. 2016. Website Fingerprinting at Internet Scale. In Proc. of NDSS'16.
[30]
O. M. Parki, A. Vedaldi, and A. Zisserman. 2015. Deep Face Recognition. In British Machine Vision Association.
[31]
V. Rimmer, D. Preuveneers, M. Juarez, T. V. Goethem, and W. Joosen. 2018. Automated Website Fingerprinting through Deep Learning. In Proc. of NDSS'18.
[32]
F. Schroff, D. Kalenichenko, and J. Philbin. 2015. FaceNet: A Unified Embedding for Face Recognition and Clustering. In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[33]
C. Shorten and T. M. Khoshgoftaar. 2019. A Survey on Image Data Augmentation for Deep Learning. Journal of Big Data 6, 60 (2019).
[34]
A. Shusterman, L. Kang, Y. Haskal, Y. Meltser, P. Mittal, Y. Oren, and Y. Yarom. 2019. Robust Website Fingerprinting Through the Cache Occupancy Channel. In Proc. of USENIX Security'19.
[35]
P. Sirinam, M. Imani, M. Juarez, and M. Wright. 2018. Deep Fingerprinting: Understanding Website Fingerprinting Defenses with Deep Learning. In Proc. of ACM CCS'18.
[36]
Payap Sirinam, Nate Mathews, Mohammad Saidur Rahman, and Matthew Wright. 2019. Triplet Fingerprinting: More Practical and Portable Website Fingerprinting with N-shot Learning. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security. 1131--1148.
[37]
B. Sun, J. Feng, and K. Saenko. 2016. Return of Frustratingly Easy Domain Adaption. In Proc. of AAAI Conference on Artificial Intelligence.
[38]
Y. Taigman, M. Yang, M. A. Ranzato, and L. Wolf. 2014. Deepface: Closing the gap to human-level performance in face verification. In Proc. of IEEE CVPR'14.
[39]
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell. 2017. Adversarial Discriminative Domain Adaptation. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[40]
E. Tzeng, J. Hoffman, N. Zhang, K. Saenko, and T. Darrell. 2014. Deep domain confusion: Maximizing for domain invariance. (2014). https://arxiv.org/pdf/1412.3474.pdf.
[41]
Tao Wang. 2020. High Precision Open-World Website Fingerprinting. In Proc. of IEEE S&P'20.
[42]
Tao Wang, Xiang Cai, Rishab Nithyanand, Rob Johnson, and Ian Goldberg. 2014. Effective attacks and provable defenses for website fingerprinting. In 23rd {USENIX} Security Symposium ({USENIX} Security 14). 143--157.
[43]
Tao Wang, Xiang Cui, Rishab Nithyannand, Rob Johnson, and Ian Goldberg. 2014. Effective Attacks on Provable Denfenses for Website Fingerprinting. In Proc. of 23rd USENIX Security Symposium.
[44]
T. Wang and I. Goldberg. 2016. On Realistically Attacking Tor with Website Fingerprinting. In Proc. of PETS'16.
[45]
T. Wang and I. Goldberg. 2017. Walkie-Talkie: An Efficient Defense Against Passive Website Fingerprinting Attacks. In Proc. of USENIX Security'17.
[46]
Y. Xu, T. Wang, Q. Li, Q. Gong, Y. Chen, and Y. Jiang. 2018. A Multi-Tab Website Fingerprinting Attack. In Proc. of ACSAC'18.
[47]
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. 2014. How transferable are features in deep neural networks?. In Advances in neural information processing systems. 3320--3328.
[48]
Zhi-Hua Zhou and Ji Feng. 2017. Deep Forest: Towards an Alternative to Deep Neural Networks. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. 3553--3559.
[49]
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He. 2020. A comprehensive survey on transfer learning. Proc. IEEE (2020).

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  • (2025)Few-Shot Website Fingerprinting With Distribution CalibrationIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2024.341101422:1(632-648)Online publication date: Jan-2025
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    cover image ACM Conferences
    CODASPY '21: Proceedings of the Eleventh ACM Conference on Data and Application Security and Privacy
    April 2021
    348 pages
    ISBN:9781450381437
    DOI:10.1145/3422337
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 26 April 2021

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    Author Tags

    1. adversarial domain adaption
    2. encrypted traffic
    3. transfer learning

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    • (2025)Few-Shot Website Fingerprinting With Distribution CalibrationIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2024.341101422:1(632-648)Online publication date: Jan-2025
    • (2024)Understanding and Improving Video Fingerprinting Attack Accuracy under Challenging ConditionsProceedings of the 23rd Workshop on Privacy in the Electronic Society10.1145/3689943.3695045(141-154)Online publication date: 20-Nov-2024
    • (2024)VoiceAttack: Fingerprinting Voice Command on VPN-protected Smart Home SpeakersProceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3671127.3698171(55-65)Online publication date: 29-Oct-2024
    • (2024)Contrastive Fingerprinting: A Novel Website Fingerprinting Attack over Few-shot TracesProceedings of the ACM Web Conference 202410.1145/3589334.3645575(1203-1214)Online publication date: 13-May-2024
    • (2024)MetaRockETC: Adaptive Encrypted Traffic Classification in Complex Network Environments via Time Series Analysis and Meta-LearningIEEE Transactions on Network and Service Management10.1109/TNSM.2024.335008021:2(2460-2476)Online publication date: Apr-2024
    • (2024)Robust App Fingerprinting Over the AirIEEE/ACM Transactions on Networking10.1109/TNET.2024.344862132:6(5065-5080)Online publication date: Dec-2024
    • (2024)Exploring the Capabilities and Limitations of Video Stream Fingerprinting2024 IEEE Security and Privacy Workshops (SPW)10.1109/SPW63631.2024.00008(28-39)Online publication date: 23-May-2024
    • (2024)Unsupervised and Adaptive Tor Website FingerprintingSecurity and Privacy in Communication Networks10.1007/978-3-031-64954-7_11(209-229)Online publication date: 15-Oct-2024
    • (2023)A Practical Website Fingerprinting Attack via CNN-Based Transfer LearningMathematics10.3390/math1119407811:19(4078)Online publication date: 26-Sep-2023
    • (2023)SMART: A Lightweight and Reliable Multi-Path Transmission Model against Website Fingerprinting AttacksElectronics10.3390/electronics1207166812:7(1668)Online publication date: 31-Mar-2023
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