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EnglishEncryption 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.
Users’ Fingerprinting Techniques from TCP Traffic / Vassio, Luca; Giordano, Danilo; Trevisan, Martino; Mellia, Marco; Couto da Silva, Ana Paula. - ELETTRONICO. - (2017). ((Intervento presentato al convegno ACM SIGCOMM Workshop on Big Data Analytics and Machine Learning for Data Communication Networks tenutosi a Los Angeles, California, USA nel August 21 - 25, 2017.
| Titolo: | Users’ Fingerprinting Techniques from TCP Traffic | |
| Autori: | ||
| Data di pubblicazione: | 2017 | |
| Abstract: | Encryption at the application layer is often promoted to protect privacy, i.e., to prevent someon...e 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. | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| user-fingerprint.pdf | Versione finale articolo | 2. Post-print / Author's Accepted Manuscript | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
| user-fingerprint (1).pdf | Camera Ready | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
http://hdl.handle.net/11583/2674705
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