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Introducing filighting and the future of DFIR tools, part 3 – more examples

April 11, 2015 in Clustering, Forensic Analysis, Visualisation

I have been toying around with the script trying it on various folders and the results are quite promising.

Here is a bunch of examples – screenshots + interactive demos. Note that some JSON files may take a long time to load so please be patient.

  • Opera 26
    • Quite a nice graph – all files had at least one reference

cluster_opera26

  • Firefox 35
    • Quite a nice graph as well – all files had at least one reference

cluster_firefox

  • Office 15
    • There is so many files that it is not very readable
    • BUT out of 3K+ files, only 17 didn’t have any reference!

cluster_office15

  • Notepad ++
    • Probably the worst case I have seen so far – lots of clusters and orphaned files

cluster_notepadplus

  • VMWare 11
    • Not too bad, lot of files are referenced, just a few stand out

cluster_vmware

Introducing filighting and the future of DFIR tools, part 2

April 11, 2015 in Clustering, Forensic Analysis, Software Releases, Visualisation

In my yesterday’s post I described a simple clustering algorithm that could be used to group files that contain references to each other. Today I am posting the source code of the program that generated the data in my last post, together with a demo that shows how powerful such clustering could be if combined with proper visualization techniques.

In the example I have shown, I used a relatively small folder where Total Commander was installed. The resulting cluster looks like this:

cluster1You can play with it interactively here.

Imagine that someone adds files to the Total Commander folder. Since they are not referenced by any other file in this folder, they will create separate clusters. After adding 3 such files:

  • orphan1.txt
  • orphan2.txt
  • orphan3.txt

we get the following clusters:

cluster2You can play with it interactively here (you need to drag the orphans away to get the same result as shown on the screenshot).

Finally, we can imagine that a hacker of malware creates a couple of files that are perhaps referencing each other. An example could be:

  • config.bin
  • keystrokes.txt
  • malware.exe – referencing keystrokes.txt and config.bin

If we now cluster this directory, we will get something like this:

cluster3The ‘malware’ files clearly stand out.

You can play with it interactively here (again, you need to drag the nodes away to get the same result as shown on the screenshot).

For more examples see part 3.

I believe there is a lot of opportunities in leveraging clustering to reduce the amount of data we need to analyze and to improve user experience by introducing new ways to look at data. There are a lot of visualization techniques that are not used in forensic software today and it is a pity. Clustering adds an extra dimension on top of a timeline and structure imposed by the organization of a file system – we can only hope that forensic software of the future will take this into account.

For inspiration and really amazing examples of visualization go to https://github.com/mbostock/d3/wiki/Gallery. I used the very same script to create the interactive demos referenced by this post.

The source code of the filighter script that generates these clusters is here.