Title :
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
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;
Conference_Titel :
Communications and Networking in China (CHINACOM), 2013 8th International ICST Conference on
Conference_Location :
Guilin
DOI :
10.1109/ChinaCom.2013.6694639
