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Detection and Analysis of Tor Onion Services

 
: Steinebach, Martin; Schäfer, Marcel; Karakuz, Alexander; Brandl, Katharina; Yannikos, York

:

Association for Computing Machinery -ACM-:
ARES 2019, 14th International Conference on Availability, Reliability and Security. Proceedings : Canterbury, CA, United Kingdom, August 26 - 29, 2019
New York: ACM, 2019
ISBN: 978-1-4503-7164-3
Art. 66, 10 S.
International Conference on Availability, Reliability and Security (ARES) <14, 2019, Canterbury>
Englisch
Konferenzbeitrag
Fraunhofer SIT ()
Fraunhofer CESE ()

Abstract
Tor onion services can be accessed and hosted anonymously on the Tor network. We analyze the protocols, software types, popularity and uptime of these services by collecting a large amount of .onion addresses. Websites are crawled and clustered based on their respective language. In order to also determine the amount of unique websites a de-duplication approach is implemented. To achieve this, we introduce a modular system for the real-time detection and analysis of onion services. Address resolution of onion services is realized via descriptors that are published to and requested from servers on the Tor network that volunteer for this task. We place a set of 20 volunteer servers on the Tor network in order to collect .onion addresses. The analysis of the collected data and its comparison to previous research provides new insights into the current state of Tor onion services and their development. The service scans show a vast variety of protocols with a significant increase in the popularity of anonymous mail servers and Bitcoin clients since 2013. The popularity analysis shows that the majority of Tor client requests is performed only for a small subset of addresses. The overall data reveals further that a large amount of permanent services provide no actual content for Tor users. A significant part consists instead of bots, services offered via multiple domains, or duplicated websites for phishing attacks. The total amount of onion services is thus significantly smaller than current statistics suggest.

: http://publica.fraunhofer.de/dokumente/N-565419.html