Papers by Eric Chan-Tin

Identifying Webbrowsers in Encrypted Communications

If you are a website, it is fairly easy to identify the web browser in use by each of your visitors, even if they take steps to suppress the blatant things like the User-Agent header. [1] [2] It is so easy, in fact, that researchers typically try to make it harder for themselves, trying instead to identify individual users even as they move around, change IP addresses, flush their cookies, etc. [3] [4]

If you are a passive eavesdropper in between the browser and the website, and the network traffic is encrypted, and particularly if you are isolated from the client’s IP address by anonymizing relays (e.g. Tor), the task should logically be much harder. Or is it? The authors of this short paper did the most obvious thing: capture packet traces and throw them at an off-the-shelf machine classifier. The feature vectors seen by the machine classifier are not described as clearly as I’d like, but I think they divided the trace into equal-length intervals and aggregated packet sizes in each direction in each interval; this is also one of the most basic and obvious things to do (the future work bit talks a little about better feature engineering). Despite the lack of tuning, they report 70–90% classification accuracy on a four-way choice among browsers (Chrome, Firefox, IE, Tor Browser) and 30–80% accuracy for a 13-way choice among browser and plugin combinations (by which they seem to mean whether or not JavaScript and Flash were enabled) (note that for a 13-way choice, chance performance would be 7.7% accuracy).

This is a short workshop paper, so it is to be expected that the results are a little crude and have missing pieces. The authors already know they need to tune their classifier. I hope someone has told them about ROC curves; raw accuracies make me grind my teeth. Besides that, the clear next step is to figure out what patterns the classifiers are picking up on, and then how to efface those patterns. I think it’s quite likely that the signal they see depends on gross characteristics of the different HTTP implementations used by each browser; for instance, at time of publication, Chrome implemented SPDY and QUIC, and the others didn’t.

The paper contains some handwaving in the direction of being able to fingerprint individual users with this information, but I’d want to see detectable variation among installations of the same browser before I’d be convinced that’s possible.