The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Inferring the source of encrypted HTTP connections
Abstract
We examine the effectiveness of two traffic analysis techniques for identifying encrypted HTTP streams. The techniques are based upon classification algorithms, identifying encrypted traffic on the basis of similarities to features in a library of known profiles. We show that these profiles need not be collected immediately before the encrypted stream; these methods can be used to identify traffic observed both well before and well after the library is created. We give evidence that these techniques will exhibit the scalability necessary to be effective on the Internet. We examine several methods of actively countering the techniques, and we find that such countermeasures are effective, but at a significant increase in the size of the traffic stream. Our claims are substantiated by experiments and simulation on over 400,000 traffic streams we collected from 2,000 distinct web sites during a two month period.
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Cites
Tor: The Second-Generation Onion Router
Dingledine, R., Mathewson, N., & Syverson, P. (2004) · doi:10.21236/ada465464 · Cited by 4,104
Statistical identification of encrypted Web browsing traffic
Sun, Q., Simon, D. R., Wang, Y.-M., Russell, W., Padmanabhan, V. N., & Qiu, L. (2005) · Proceedings 2002 IEEE Symposium on Security and Privacy · doi:10.1109/secpri.2002.1004359 · Cited by 377
Cited by
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
Robust Smartphone App Identification via Encrypted Network Traffic Analysis
Taylor, V. F., Spolaor, R., Conti, M., & Martinovic, I. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2737970 · Cited by 439
“[13] outperformed the aforementioned work by Liberatore and Levine [12] by presenting a method that relies on common text mining techniques performed over the normalized frequency distribution of observable IP packet sizes.”
BackgroundMethodFlowPrint: 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
A Critical Evaluation of Website Fingerprinting Attacks
Juarez, M., Afroz, S., Acar, G., Diaz, C., & Greenstadt, R. (2014) · Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2660267.2660368 · Cited by 327
Automated Website Fingerprinting through Deep Learning
Rimmer, V., Preuveneers, D., Juarez, M., Goethem, T. V., & Joosen, W. (2018) · Proceedings 2018 Network and Distributed System Security Symposium · doi:10.14722/ndss.2018.23105 · Cited by 319
Metadata
- Authors
- Liberatore, M., & Levine, B. N.
- Venue
- Proceedings of the 13th ACM conference on Computer and communications security
- Publisher
- ACM
- Published
- 2006-10-30
- Pages
- 255-263
- ISSN
- 1543-7221 (ISSN-L)
- OpenAlex
- W2135088779
- Cited by
- 413
- References
- 2
- FWCI
- 6.00
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Liberatore, M., & Levine, B. N. (2006). Inferring the source of encrypted HTTP connections. Proceedings of the 13th ACM conference on Computer and communications security. https://doi.org/10.1145/1180405.1180437
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