![]() ![]() ![]() Further performance gains are also accomplished by tuning the thresholds ensuring progressive censoring. Results highlight a general improvement over the flat approach in terms of all the classification metrics. only the first segments of traffic aggregate are used to take a decision). The proposed framework is investigated in both the usual TC setup and its “early” variant (i.e. Capitalizing a public dataset released in 2017 containing anonymity traffic, we provide an in-depth analysis of TC and we compare the proposed hierarchical approach with a flat counterpart. In this paper we propose TC of anonymity tools (and deeper, of their running services and applications) via a truly hierarchical approach. These mechanisms are challenged by the process of Traffic Classification (TC) which, other than being an important workhorse for several network management tasks, becomes a key factor in the assessment of their privacy level, both from offensive (malign) and defensive (benign) standpoints. Privacy-preserving protocols and tools are increasingly adopted by Internet users nowadays. 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%). 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. 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. 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. 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). Results show that the considered anonymity networks (Tor, I2P, JonDonym) can be easily distinguished (with an accuracy of 99.87% and 99.80%, in case of flow-based and early-TC, respectively), telling even the specific application generating the traffic (with an accuracy of 73.99% and 66.76%, in case of flow-based and early-TC, respectively). Successively, " early " TC of anonymous networks is analyzed. ![]() Additionally, the role of finer-grained features, such as the (joint) histogram of packet lengths (and inter-arrival times), is determined. Initially, flow-based TC is considered, and the effects of feature importance and temporal-related features to the network are investigated. In this paper, leveraging a public dataset released in 2017, we provide classification results with the aim of investigating to which degree the specific anonymity tool (and the traffic it hides) can be identified, when compared to the traffic of other considered anonymity tools, using five machine learning classifiers. This process is impaired or prevented by privacy-preserving protocols and tools, that encrypt the communication content, and (in case of anonymity tools) additionally hide the source, the destination, and the nature of the communication. Traffic Classification (TC) is an important tool for several tasks, applied in different fields (security, management, traffic engineering, R&D). ![]()
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