The DOI matches its Crossref record; 107 of the 107 references it cites were found in a registry or an index.
A Survey on Encrypted Network Traffic Analysis Applications, Techniques, and Countermeasures
Open accessAbstract
The adoption of network traffic encryption is continually growing. Popular applications use encryption protocols to secure communications and protect the privacy of users. In addition, a large portion of malware is spread through the network traffic taking advantage of encryption protocols to hide its presence and activity. Entering into the era of completely encrypted communications over the Internet, we must rapidly start reviewing the state-of-the-art in the wide domain of network traffic analysis and inspection, to conclude if traditional traffic processing systems will be able to seamlessly adapt to the upcoming full adoption of network encryption. In this survey, we examine the literature that deals with network traffic analysis and inspection after the ascent of encryption in communication channels. We notice that the research community has already started proposing solutions on how to perform inspection even when the network traffic is encrypted and we demonstrate and review these works. In addition, we present the techniques and methods that these works use and their limitations. Finally, we examine the countermeasures that have been proposed in the literature in order to circumvent traffic analysis techniques that aim to harm user privacy.
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Cites
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
Tor: The Second-Generation Onion Router
Dingledine, R., Mathewson, N., & Syverson, P. (2004) · doi:10.21236/ada465464 · Cited by 4,104
Moustafa, N., & Slay, J. (2015) · 2015 Military Communications and Information Systems Conference (MilCIS) · doi:10.1109/milcis.2015.7348942 · Cited by 4,033
Practical techniques for searches on encrypted data
Song, D. X., Wagner, D., & Perrig, A. (2002) · Proceeding 2000 IEEE Symposium on Security and Privacy. S&P 2000 · doi:10.1109/secpri.2000.848445 · Cited by 3,350
A Deep Learning Approach to Network Intrusion Detection
Shone, N., Ngoc, T. N., Phai, V. D., & Shi, Q. (2018) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2017.2772792 · Cited by 1,819
Deep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,768
Investigating Ad Transparency Mechanisms in Social Media: A Case Study of Facebook's Explanations
Andreou, A., Venkatadri, G., Goga, O., Gummadi, K. P., Loiseau, P., & Mislove, A. (2018) · Proceedings 2018 Network and Distributed System Security Symposium · doi:10.14722/ndss.2018.23191 · Cited by 1,477
Enck (2014) · doi:10.1145/2619091 · Cited by 1,203
Deep packet: a novel approach for encrypted traffic classification using deep learning
Lotfollahi, M., Jafari Siavoshani, M., Shirali Hossein Zade, R., & Saberian, M. (2020) · Soft Computing · doi:10.1007/s00500-019-04030-2 · Cited by 1,190
BLINC: multilevel traffic classification in the dark
Karagiannis, T., Papagiannaki, K., & Faloutsos, M. (2005) · Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications · doi:10.1145/1080091.1080119 · Cited by 997
Cited by
Huo, Y., Zhao, F., Zhang, H., Zhuang, S., & Sun, J. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/1556768 · Cited by 8
Lightweight Identification of Malicious IoT Traffic via Cross-View Knowledge Distillation
Nie, F., Liu, W., Liu, G., Gao, B., Huang, J., & Yuen, C. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3596167 · Cited by 1
Metadata
- Authors
- Papadogiannaki, E., & Ioannidis, S.
- Venue
- ACM Computing Surveys
- Published
- 2021-07-13
- Volume
- 54
- Issue
- 6
- Pages
- 1-35
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W3181596493
- Cited by
- 281
- References
- 107
- FWCI
- 11.82
- Type
- journal article
- Language
- en
- Open access
- green
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