Modern operating systems typically have thousands of files, and thousands of hyerarchically-nested folders. There are cases in which we might need to have an overview of the content of a hard drive (or a USB pendrive, or DVD), and quickly find out what size and type of files there are. This is the case during the initial phases of a forensics investigation.
While reading Greg Conti’s excellent “Security Data Visualization” I came across this wonderful piece of software: SequoiaView. It is developed at the Technical University of Eindhoven (The Netherlands)
“Ever wondered why your hard disk is full? Or what directory is taking up most of the space? When using conventional disk browsing tools, such as Windows Explorer, these questions may be hard to answer. With SequoiaView however, they can be answered almost immediately. SequoiaView uses a visualization technique called cushion treemaps to provide you with a single picture of the entire contents of your hard drive. You can use it to locate those large files that you haven’t accessed in one year, or to quickly locate the largest picture files on your drive.”

The software is available for free, but only for Windows systems. For Unix-like and Linux users, there are several options. One of them is GDMap, which is very similar to SequoiaView, but with only some basic functionality implemented. For KDE desktops, there is the powerful KDirStat (there is a Windows clone called WinDirStat). For Gnome users, there is Baobab. A nice alternative is firelight, which uses circular representation of treemaps to show similar data (only for KDE).
Wouldn’t it be nice if popular forensics packages, such as Helix, bundled these kind of tools?
The work on QRBG Service has been motivated by scientific necessity (primarily of local scientific community) of running various simulations (in cluster/Grid environments), whose results are often greatly affected by quality (distribution, nondeterminism, entropy, etc.) of used random numbers. Since true random numbers are impossible to generate with a finite state machine (such as today’s computers), scientists are forced to either use specialized expensive hardware number generators, or, more frequently, to content themselves with suboptimal solutions (like pseudo-random numbers generators).





