Traffic classification, i.e. associating network traffic to the application that generated it, is an important tool for several tasks, spanning on different fields (security, management, traffic engineering, R&D). This process is challenged by applications that preserve Internet users' privacy by encrypting the communication content, and even more by anonymity tools, additionally hiding the source, the destination, and the nature of the communication. In this paper, leveraging a public dataset released in 2017, we provide (repeatable) classification results with the aim of investigating to what degree the specific anonymity tool (and the traffic it hides) can be identified, when compared to the traffic of the other considered anonymity tools, using machine learning approaches based on the sole statistical features. To this end, four classifiers are trained and tested on the dataset: (i) Naïve Bayes, (ii) Bayesian Network, (iii) C4.5, and (iv) Random Forest. Results show that the three considered anonymity networks (Tor, I2P, JonDonym) can be easily distinguished (with an accuracy of 99.99%), telling even the specific application generating the traffic (with an accuracy of 98.00%).

Anonymity Services Tor, I2P, JonDonym: Classifying in the Dark / Montieri, Antonio; Ciuonzo, Domenico; Aceto, Giuseppe; Pescape, Antonio. - 1:(2017), pp. 81-89. (Intervento presentato al convegno 29th International Teletraffic Congress, ITC 2017 tenutosi a University of Genoa, Italia nel 2017) [10.23919/ITC.2017.8064342].

Anonymity Services Tor, I2P, JonDonym: Classifying in the Dark

Montieri, Antonio;Ciuonzo, Domenico;Aceto, Giuseppe;Pescape, Antonio
2017

Abstract

Traffic classification, i.e. associating network traffic to the application that generated it, is an important tool for several tasks, spanning on different fields (security, management, traffic engineering, R&D). This process is challenged by applications that preserve Internet users' privacy by encrypting the communication content, and even more by anonymity tools, additionally hiding the source, the destination, and the nature of the communication. In this paper, leveraging a public dataset released in 2017, we provide (repeatable) classification results with the aim of investigating to what degree the specific anonymity tool (and the traffic it hides) can be identified, when compared to the traffic of the other considered anonymity tools, using machine learning approaches based on the sole statistical features. To this end, four classifiers are trained and tested on the dataset: (i) Naïve Bayes, (ii) Bayesian Network, (iii) C4.5, and (iv) Random Forest. Results show that the three considered anonymity networks (Tor, I2P, JonDonym) can be easily distinguished (with an accuracy of 99.99%), telling even the specific application generating the traffic (with an accuracy of 98.00%).
2017
9780988304536
Anonymity Services Tor, I2P, JonDonym: Classifying in the Dark / Montieri, Antonio; Ciuonzo, Domenico; Aceto, Giuseppe; Pescape, Antonio. - 1:(2017), pp. 81-89. (Intervento presentato al convegno 29th International Teletraffic Congress, ITC 2017 tenutosi a University of Genoa, Italia nel 2017) [10.23919/ITC.2017.8064342].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/720075
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