Change search
ReferencesLink to record
Permanent link

Direct link
Torminator: A Tor fingerprinting suite: Or how the Tor-network might get a surprise attack from the future. “I’ll be back” – The Terminator
KTH, School of Electrical Engineering (EES), Communication Networks.
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Tor is a very popular anonymisation software and network. For which we

created Torminator, a fingerprinting suite written in the Java programming language.

Fingerprinting is an attack type applicable to Tor utilising side-channel

information from the network packets. With side-channel data, we can analytically

access information that purportedly been hidden by design by Tor. Because

Tor is a low-latency, low-overhead by design, it will leak communication

patterns with intermediate (thus total) communication size. In our case this

may able us figure out to which site/service the Tor user is using. This means

that anyone with access to user’s traffic can use the fingerprinting attack to

partly compromise the provided anonymity. By investigating such attacks, it

may help us to better understand how to withstand and resist attacks from

powerful adversaries such as state agencies.

Torminator automatises the process for gathering fingerprints. It uses the

official Tor Browser through its GUI to enter websites to recreate the real world

scenario. This gives us real and reliable fingerprints without having to employ

a human to do anything, as Torminator simulates user interaction on Tor

Browser for us. We can also give Torminator a list of websites to fingerprint,

making it easy to generate lots of fingerprints for a great number given sites.

A contribution of Torminator, is that we improved on the previous de facto

standard of the fingerprints collected from the available tools from previous

works. We have gathered fingerprints and have now a dataset of 65792 fingerprints.

Fingerprints like these can be used with machine learning techniques

to teach a machine to recognise web-pages by reading the packet size and directions

saved in the fingerprint files.

Place, publisher, year, edition, pages
2015. , 59 p.
EES Examensarbete / Master Thesis, XR-EE-LCN 2014:012
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
URN: urn:nbn:se:kth:diva-163201OAI: diva2:799167
Educational program
Master of Science in Engineering - Computer Science and Technology; Master of Science - Computer Science
Available from: 2015-03-30 Created: 2015-03-30 Last updated: 2015-03-30Bibliographically approved

Open Access in DiVA

fulltext(691 kB)162 downloads
File information
File name FULLTEXT01.pdfFile size 691 kBChecksum SHA-512
Type fulltextMimetype application/pdf

By organisation
Communication Networks
Other Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 162 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 622 hits
ReferencesLink to record
Permanent link

Direct link