Using Traffic Analysis to Identify the Second Generation Onion Router

Barker, John; Hannay, Peter; Szewczyk, Patryk

doi:10.1109/euc.2011.76

Cited by 36 publications

(10 citation statements)

References 15 publications

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“…The analysis of ATs has been initially carried in private networks, e.g. with the aim of discriminating between HTTPS and Tor traffic [13]. In detail, by leveraging a dataset made of (i) regular HTTPS traffic, (ii) HTTP and (iii) HTTPS over a private Tor network, authors show that HTTP/HTTPS traffic over Tor can be detected with ≥ 93% accuracy, employing Random Forest (RF), C4.5, and AdaBoost classifiers.…”

Section: (Flat) Traffic Classification Of Anonymity Toolsmentioning

confidence: 99%

“…4 According to the above dataset description, we tackle the problem of classification of ATs traffic assuming that we are in the presence of AT traffic only, similarly to [5]. The depicted scenario refers to a context where an upstream classifier has been able to separate AT traffic from clear or standard-encrypted one (e.g., as shown by [13] for Tor network). Hence, the aim of the proposed approach is to assess discrimination of anonymity services and related applications once this AT traffic has been separated from other traffic.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…+3.16%) gains are experienced at L2 and L3, respectively. 13 The details of optimized HC are reported in Fig. 5a, where for each classifier node the employed classifier and the number of features are reported.…”

Section: Fine-grained Optimizationmentioning

confidence: 99%

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A Dive into the Dark Web: Hierarchical Traffic Classification of Anonymity Tools

Montieri

Ciuonzo

Bovenzi

et al. 2020

IEEE Trans. Netw. Sci. Eng.

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Privacy-preserving protocols and tools are increasingly adopted by Internet users nowadays. 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. In this paper we propose TC of anonymity tools (and deeper, of their running services and applications) via a truly hierarchical approach. 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. The proposed framework is investigated in both the usual TC setup and its "early" variant (i.e. only the first segments of traffic aggregate are used to take a decision). Results highlight a general improvement over the flat approach in terms of all the classification metrics. Further performance gains are also accomplished by tuning the thresholds ensuring progressive censoring. Finally, fine-grain performance investigation allows to (a) demonstrate lower severity of errors incurred by the hierarchical approach (as opposed to the flat case) and (b) highlight poorly-classifiable services/applications of each anonymity tool, gathering useful feedback on their privacy-level.

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Section: (Flat) Traffic Classification Of Anonymity Toolsmentioning

confidence: 99%

Section: Dataset Descriptionmentioning

confidence: 99%

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A Dive into the Dark Web: Hierarchical Traffic Classification of Anonymity Tools

Montieri

Ciuonzo

Bovenzi

et al. 2020

IEEE Trans. Netw. Sci. Eng.

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show abstract

“…Differentiate Tor Traffic from Encrypted Traffic: Barker et al [51] showed that traffic from the Tor network can be differentiated form encrypted traffic in the network. They captured regular HTTPS, Tor HTTPS and HTTP traffic routed through Tor and analyzed their packet sizes and developed an unsupervised machine learning (ML) classifier that operates only on packet size attribute with 97.54% true positive (TP) and 1.06% false positive (FP) rates.…”

Section: Tor Traffic Detectionmentioning

confidence: 99%

“…Barker et al [51] collected Tor network traces by developing a complete Tor setup. Firefox running on Ubuntu was used on all machines.…”

Section: Private Setup Connected With Tormentioning

confidence: 99%

Shedding Light on the Dark Corners of the Internet: A Survey of Tor Research

Saleh

Qadir

Ilyas

2018

Journal of Network and Computer Applications

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Anonymity services have seen high growth rates with increased usage in the past few years. Among various services, Tor is one of the most popular peer-to-peer anonymizing service. In this survey paper, we summarize, analyze, classify and quantify 26 years of research on the Tor network. Our research shows that 'security' and 'anonymity' are the most frequent keywords associated with Tor research studies. Quantitative analysis shows that the majority of research studies on Tor focus on 'deanonymization' the design of a breaching strategy. The second most frequent topic is analysis of path selection algorithms to select more resilient paths. Analysis shows that the majority of experimental studies derived their results by deploying private testbeds while others performed simulations by developing custom simulators. No consistent parameters have been used for Tor performance analysis. The majority of authors performed throughput and latency analysis.

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Handling Exit Node Vulnerability in Onion Routing with a Zero-Knowledge Proof

Voloch¹,

Hajaj²

2022

Information Integration and Web Intelligence

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Using Traffic Analysis to Identify the Second Generation Onion Router

Cited by 36 publications

References 15 publications

A Dive into the Dark Web: Hierarchical Traffic Classification of Anonymity Tools

A Dive into the Dark Web: Hierarchical Traffic Classification of Anonymity Tools

Shedding Light on the Dark Corners of the Internet: A Survey of Tor Research

Handling Exit Node Vulnerability in Onion Routing with a Zero-Knowledge Proof

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