[IP] Microphones placed near keyboards can record keystrokes.
Begin forwarded message:
From: Bradley Malin <malin@xxxxxxxxxx>
Date: September 13, 2005 5:31:26 PM EDT
To: dave@xxxxxxxxxx, joehall@xxxxxxxxx
Subject: Re: [IP] Microphones placed near keyboards can record
keystrokes.
I just read the paper and I agree, this is good research. However,
while it's flashy, it's not much of a breakthrough. At its
foudation, this paper combines two known concepts:
1) Asonov's finding (their reference [1]) that microphones capture
different sounds for different keys, and
2) Probabilistic models (i.e. HMM's and mixture models) for resolving
patterns in the acoustic components akin to speech recognition models.
Novelty, yes. Breakthrough, no.
Also note, while the authors don't exactly lie, they do sweep certain
aspects of their claims under the rug. Specifically, they claim they
can detect words in an unsupervised setting (i.e. they don't train a
classifier for words like Asonov did). However, this is not really
true outside of pedantic machine learning jargon. A quick sketch of
my claim follows.
Every keytype pattern which they extract is compared to an English
dictionary. So, really what they do is input an acoustic emanation
(space delimited) and convert into a string where each sound gets a
character. Then, they compare each sequence of characters to words
in the dictionary and return the English word with the most similar
pattern. Thus, while Asonov compared his acoustic patterns to
"trained" neural-net classifiers of acoustics, the authors of this
paper are comparing their sequences of characters to a standardized
set of sequences of characters (i.e. English words).
One more thing, their "unsupervised" learning model only accounts for
words which are in the dictionary. If the word is not in the
dictionary, then the authors move to a "supervised" (or trained)
system. Specifically, they "use the previously obtained corrected
results [word matches] as labeled training samples". So, password
snarfing is achievable, but you may have to train your system against
each user.
hopefully not offending anyone,
-brad
================================================
Bradley Malin, PhD candidate
Carnegie Mellon University
School of Computer Science
Data Privacy Laboratory
David Farber wrote:
Begin forwarded message:
From: Joseph Lorenzo Hall <joehall@xxxxxxxxx>
Date: September 13, 2005 4:25:43 PM EDT
To: Dave Farber <dave@xxxxxxxxxx>
Subject: Microphones placed near keyboards can record keystrokes.
Reply-To: joehall@xxxxxxxxx
Here's a second try at submitting this one to IP... : )
Since I last sent this, this research has made [Ed Felten's blog][6],
[Bruce Schneier's blog][7] and [Slashdot][8]. I can imagine some
IPers would have interesting things to say. My own thoughts are
[here][9]. best, Joe
----
<http://tygar-blog.com/2005/09/keyboard-acoustic-emanations-
revisited.html>
## September 2, 2005
### Keyboard Acoustic Emanations Revisited
Microphones placed near keyboards can record keystrokes. [Li
Zhuang][1], [Feng Zhou][2], and [I (Doug Tygar)][3] have developed a
set of algorithms for recreating the material typed directly from the
keystrokes. Unlike previous approaches, our algorithms require no
information about the typist, keyboard, room, or text typed. Unlike
previous approaches, our algorithms do not require any "labeled
training data" (matching acoustic recordings to the actual text typed
by a particular typist.) In contrast, our algorithm can use data from
a cheap microphone in the room with a typist, collect ten minutes
worth of data, and the algorithm will be able to recover the typed
text. In fact, once our algorithm has ten minutes worth of typed
English text, it can recover arbitrary text, such as passwords. Even
if the typist uses a "quiet keyboard", we can still recover the
text. And our work further suggests that the microphone need not be
placed in a room -- a parabolic microphone outside the room would work
equally well at recovering the signals.
Our paper on this work will appear in November 2005 at the ACM
Conference on Computer and Communications Security.
A preprint of our paper describing this work is available at
[keyboard-emanations.org][4]. Copies of other papers by me are
available at [my publications web site][5].
Doug Tygar 9/02/2005 08:33:00 AM
[1]: http://www.cs.berkeley.edu/~zl/
[2]: http://www.cs.berkeley.edu/~zf/
[3]: http://www.cs.berkeley.edu/~tygar/
[4]: http://keyboard-emanations.org/
[5]: http://www.cs.berkeley.edu/~tygar/publications.htm
[6]: http://www.freedom-to-tinker.com/?p=893
[7]: http://www.schneier.com/blog/archives/2005/09/
snooping_on_tex.html
[8]: http://it.slashdot.org/article.pl?
sid=05/09/13/1644259&tid=172&tid=218
[9]: http://josephhall.org/nqb2/index.php/2005/09/04/mic_strokes
--
Joseph Lorenzo Hall
UC Berkeley, SIMS PhD Student
<http://josephhall.org/>
This email is written in [markdown] - an easily-readable and parseable
text format.
[markdown]: http://daringfireball.net/projects/markdown/
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