The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Analyzing Android Encrypted Network Traffic to Identify User Actions
Open accessAbstract
Mobile devices can be maliciously exploited to violate the privacy of people. In most attack scenarios, the adversary takes the local or remote control of the mobile device, by leveraging a vulnerability of the system, hence sending back the collected information to some remote web service. In this paper, we consider a different adversary, who does not interact actively with the mobile device, but he is able to eavesdrop the network traffic of the device from the network side (e.g., controlling a Wi-Fi access point). The fact that the network traffic is often encrypted makes the attack even more challenging. In this paper, we investigate to what extent such an external attacker can identify the specific actions that a user is performing on her mobile apps. We design a system that achieves this goal using advanced machine learning techniques. We built a complete implementation of this system, and we also run a thorough set of experiments, which show that our attack can achieve accuracy and precision higher than 95%, for most of the considered actions. We compared our solution with the three state-of-the-art algorithms, and confirming that our system outperforms all these direct competitors.
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Cites
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Peek-a-Boo, I Still See You: Why Efficient Traffic Analysis Countermeasures Fail
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Cited by
FS-Net: A Flow Sequence Network For Encrypted Traffic Classification
Liu, C., He, L., Xiong, G., Cao, Z., & Li, Z. (2019) · IEEE INFOCOM 2019 - IEEE Conference on Computer Communications · doi:10.1109/infocom.2019.8737507 · Cited by 630
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
“[28] and Saltaformaggio et al.”
Self-citationShen, 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
A Survey on Encrypted Network Traffic Analysis Applications, Techniques, and Countermeasures
Papadogiannaki, E., & Ioannidis, S. (2022) · ACM Computing Surveys · doi:10.1145/3457904 · Cited by 281
Peek-a-boo: i see your smart home activities, even encrypted!
Acar, A., Fereidooni, H., Abera, T., Sikder, A. K., Miettinen, M., Aksu, H., Conti, M., Sadeghi, A.-R., & Uluagac, S. (2020) · Proceedings of the 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks · doi:10.1145/3395351.3399421 · Cited by 264
Self-citation
Metadata
- Authors
- Conti, M., Mancini, L. V., Spolaor, R., & Verde, N. V.
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2016-01
- Volume
- 11
- Issue
- 1
- Pages
- 114-125
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W2295321789
- Cited by
- 266
- References
- 35
- FWCI
- 20.02
- Type
- journal article
- Language
- en
- Open access
- green
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