Luca Vassio
Marco Mellia
ABSTRACT
Encryption at the application layer is often promoted to protect privacy, i.e., to prevent someone in the network from observing users' communications. In this work we explore how to build a profile for a target user by observing only the names of the services contacted during browsing, names that are still not encrypted and easily accessible from passive probes. Would it be possible to uniquely identify a target user from a large population that accesses the same network?
Aiming at verifying if and how this is possible, we propose and compare three methodologies to compute similarities between users' profiles. We use real data collected in networks, evaluate and discuss performance and the impact of quality of data being used. To this end, we propose a machine learning methodology to extract the services intentionally requested by users, which turn out to be important for the profiling purpose. Results show that the classification problem can be solved with good accuracy (up to 94%), provided some ingenuity is used to build the model.
Supplemental Material
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Digital Library
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- M. Trevisan, A. Finamore, M. Mellia, M. Munafo, and D. Rossi. 2017. Traffic Analysis with Off-the-Shelf Hardware: Challenges and Lessons Learned. IEEE Communications Magazine 55, 3 (March 2017), 163--169. Google ScholarDigital Library
- L. Vassio, I. Drago, and M. Mellia. 2016. Detecting User Actions from HTTP Traces: Toward an Automatic Approach. In Proceedings of the TRAC. IEEE, 50--55.Google Scholar
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Index Terms
- Users' Fingerprinting Techniques from TCP Traffic
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