The DOI matches its Crossref record, with a small difference in title or year; 36 of the 39 references it cites were found in a registry or an index, and 1 are not checked yet.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Flagged first, then by citations
2 of the 39 references couldn’t be matched to a known paper.
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).
Cited by
Sorted by citations
Lin, X., Xiong, G., Gou, G., Li, Z., Shi, J., & Yu, J. (2022) · Proceedings of the ACM Web Conference 2022 · doi:10.1145/3485447.3512217 · Cited by 599
Shen, M., Zhang, J., Zhu, L., Xu, K., & Du, X. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3050608 · Cited by 392
FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic
van Ede, T., Bortolameotti, R., Continella, A., Ren, J., Dubois, D. J., Lindorfer, M., Choffnes, D., van Steen, M., & Peter, A. (2020) · Proceedings 2020 Network and Distributed System Security Symposium · doi:10.14722/ndss.2020.24412 · Cited by 339
Machine Learning-Powered Encrypted Network Traffic Analysis: A Comprehensive Survey
Shen, M., Ye, K., Liu, X., Zhu, L., Kang, J., Yu, S., Li, Q. L., & Xu, K. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3208196 · Cited by 306
Lashkari, A. H., Kaur, G., & Rahali, A. (2020) · International Conference on Communication and Network Security (ICCNS) · doi:10.1145/3442520.3442521 · Cited by 181
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
