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I looked into a fair amount of this sort of normalization back when I was playing with dotplots. The idea was to upgrade from simple Levenshtein string comparison (with no knowledge of variable length x86 instructions, pointers that shift from compile to compile, etc) to something with at least some domain specific knowledge. What I found, somewhat surprisingly, was that dumb string comparison was more than enough. In fact, when I compared pre-patch and post-patch builds, it was easy to directly see when content was added, removed, shifted in location, etc. Joxean's going to have much the same result -- as basic as his similarity metric is, he'll get the broad strokes just fine.
Ultimately the best approach is to build a graph of how functions interact and measure graph isomorphism, but of course Halvar figured that out years ago :)
On Tue, Jan 5, 2010 at 3:41 PM, T Biehn <email@example.com> wrote:
> Wouldn't it be more useful to the sec community to have a algorithm
> that abstracts at the -interpreted- content level? That is when
> analyzing binaries I wouldn't think that this would classify two with
> near identical functionality together, even though it is removing a
> significant chunk of information during the hash pass.
> I would largely assume that your algorithm, as is, works best on
> uncompressed bitmaps. Is there something I'm missing?
> On Sun, Jan 3, 2010 at 6:37 AM, Joxean Koret <firstname.lastname@example.org> wrote:
> > Hi all,
> > I'm happy to announce the very first public release of the open source
> > project DeepToad, a tool for computing fuzzy hashes from files.
> > DeepToad can generate signatures, clusterize files and/or directories
> > and compare them. It's inspired in the very good tool ssdeep  and, in
> > fact, both projects are very similar.
> > The complete project is written in pure python and is distributed under
> > the LGPL license .
> > Links:
> > Project's Web Page http://code.google.com/p/deeptoad/
> > Download Web Page http://code.google.com/p/deeptoad/downloads/list
> > Wiki http://code.google.com/p/deeptoad/w/list
> > References:
> >  http://ssdeep.sourceforge.net/
> >  http://www.gnu.org/licenses/lgpl.html
> > Regards && Happy new year!
> > Joxean Koret
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