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Adaptive encrypted traffic fingerprinting with bi-directional dependence
Open accessAbstract
Recently, network traffic analysis has been increasingly used in various applications including security, targeted advertisements, and network management. However, data encryption performed on network traffic poses a challenge to these analysis techniques. In this paper, we present a novel method to extract characteristics from encrypted traffic by utilizing data dependencies that occur over sequential transmissions of network packets. Furthermore, we explore the temporal nature of encrypted traffic and introduce an adaptive model that considers changes in data content over time. We evaluate our analysis on two packet encrypted applications: website fingerprinting and mobile application (app) fingerprinting. Our evaluation shows how the proposed approach outperforms previous works especially in the open-world scenario and when defense mechanisms are considered.
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“These methods highly rely on expert-designed features and have limited generalization ability.”
From ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification (2022) · cites this paper as [1]
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565
Scikit-learn: Machine Learning in Python
(2011) · Journal of Machine Learning Research · doi:10.5555/1953048.2078195 · Cited by 8,539
Tor: The Second-Generation Onion Router
Dingledine, R., Mathewson, N., & Syverson, P. (2004) · doi:10.21236/ada465464 · Cited by 4,104
Supervised and Unsupervised Discretization of Continuous Features
Dougherty, J., Kohavi, R., & Sahami, M. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50032-3 · Cited by 2,005
Website Fingerprinting at Internet Scale
Panchenko, A., Lanze, F., Zinnen, A., Henze, M., Pennekamp, J., Wehrle, K., & Engel, T. (2016) · Proceedings 2016 Network and Distributed System Security Symposium · doi:10.14722/ndss.2016.23477 · Cited by 645
Website fingerprinting in onion routing based anonymization networks
Panchenko, A., Niessen, L., Zinnen, A., & Engel, T. (2011) · ACM Workshop on Privacy in the Electronic Society (WPES) · doi:10.1145/2046556.2046570 · Cited by 531
Peek-a-Boo, I Still See You: Why Efficient Traffic Analysis Countermeasures Fail
Dyer, K. P., Coull, S. E., Ristenpart, T., & Shrimpton, T. (2012) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2012.28 · Cited by 478
Research works printed but not linked: 10
- 10.5555/2671225.2671290 · DOI
- 10.5555/1949317.1949356 · DOI
- 10.5555/3091622.3091646 · DOI
- 10.5555/3016100.3016130 · DOI
- Hite, K. C., Ciciora, W. S., Alison, T., Beauregard, R. G., et al. System and method for delivering targeted advertisements to consumers, June 30 1998. US Patent 5,774,170.
- 10.5555/1045502.1045522 · DOI
- 10.5555/371931.371972 · DOI
- 10.5555/2028052.2028062 · DOI
- 10.5555/2671225.2671235 · DOI
- Wei, T., Zhang, Y., Xue, H., Zheng, M., Ren, C., and Song, D. Sidewinder targeted attack against android in the golden age of ad libraries. Black Hat USA 2014 (2014).
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Zhang, H., Chen, Z., Xia, W., Xiong, G., Gou, G., Li, Z., Huang, G., & Li, Y. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890501 · Cited by 1
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Li, Z., Jiang, Y., Zhao, J., Zhang, L., Tan, L., & Bu, F. (2025) · Cluster Computing · doi:10.1007/s10586-025-05159-6 · Cited by 1
Yang, H., Chen, W., Chen, W., Wang, S., Li, P., & Tian, Y. (2025) · IEEE International Conference on Privacy Computing and Data Security (PCDS) · doi:10.1109/pcds65695.2025.00045 · Cited by 1
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Metadata
- Authors
- Al-Naami, K., Chandra, S., Mustafa, A., Khan, L., Lin, Z., Hamlen, K., & Thuraisingham, B.
- Affiliations
- The University of Texas at Dallas (US)
- Venue
- Proceedings of the 32nd Annual Conference on Computer Security Applications
- Publisher
- ACM
- Published
- 2016-12-05
- Pages
- 177-188
- ISSN
- 1063-9527 (ISSN-L)
- OpenAlex
- W2560100125
- Cited by
- 145
- References
- 39
- Citations by year
- 2026: 29 · 2025: 28 · 2024: 33 · 2023: 21 · 2022: 8 · 2021: 9 · 2020: 7 · 2019: 7 · 2018: 2 · 2017: 1
- FWCI
- 1.80
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Annual Computer Security Applications Conference (publishedVersion)
- Funders
- National Security Agency (H98230-15-1-0271)
- National Science Foundation (1054629)
- The Air Force Office of Scientific Research (FA-9550-12-1-0077,FA9550-14-1-0173)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Information Systems › Digital and Cyber Forensics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- encrypted traffic analysis, traffic fingerprinting, website fingerprinting, adaptive models, defense mechanisms
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