A de-anonymize attack method based on traffic analysis

Author

Ming Song ; Gang Xiong ; Zhenzhen Li ; Junrui Peng ; Li Guo

Author_Institution

Beijing Univ. of Post & Telecommun., Beijing, China

fYear

2013

fDate

14-16 Aug. 2013

Firstpage

455

Lastpage

460

Abstract

While providing protection for users´ privacy, anonymity network has also been exploited by criminals to carry out crime anonymously. We study the problem how to break the unlinkability between the senders and recipients in order to identify the source of anonymous traffic in this paper. Tor, the most widely deployed anonymity network, is selected as our target. We develop a de-anonymize attack method based on traffic analysis and choose the {time, stream size} as features for k-means algorithm to mine the association between the first hop traffic and last hop traffic of Tor. Experiments show that our method is effective for Tor.

Keywords

Internet; computer crime; computer network security; data mining; data privacy; Internet; Tor; anonymity network protection; anonymous traffic; association mining; crime; de-anonymize attack method; k-means algorithm; traffic analysis; user privacy protection; Cryptography; IP networks; Relays; Servers; Support vector machines; Training; Watermarking; Anonymity Network; Data Mining; Tor; Traffic Analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Communications and Networking in China (CHINACOM), 2013 8th International ICST Conference on

Conference_Location

Guilin

Type

conf

DOI

10.1109/ChinaCom.2013.6694639

Filename

6694639