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L2T_PROCESS(1)	      User Contributed Perl Documentation	L2T_PROCESS(1)

NAME
       l2t_process - A small script to process the CSV output from
       log2timeline, sorts and extracts sorten dates

SYNOPSIS
       l2t_process [OPTIONS] -b CSV_FILE [DATE_RANGE]

       Where DATE_RANGE is MM-DD-YYYY or MM-DD-YYYY..MM-DD-YYYY

OPTIONS
       -b|-body CSVFILE
	       The name of the file that contains the CSV output produced by
	       log2timeline.

       -t|-tab The default input to the tool is a file that was created using
	       the CSV output module.  However, the TAB module can also be
	       used, however you will need to tell the tool that the file is
	       TAB delimited instead of comma separated, using this option.

       -i|-include
	       The tool detects possible timestomping activity against changes
	       made to MFT records (millisecond is of zero value). This option
	       makes the tool add lines that contain suspicious entries even
	       though they fall outside the supplied date filter.

       -e|-exclude
	       The tool detects possible timestomping activity against changes
	       made to MFT records (millisecond is of zero value). If this
	       option is supplied the tool will not ask the user to add the
	       lines that are suspicous yet are outside the supplied date
	       range.

       -v|-verbose
	       Making the script produce mode debug information (be more
	       verbose)

       -y      The default format for the date variable is mm-dd-yyyy, however
	       this default behavior can be changed with this option so the
	       format read is yyyy-mm-dd.

       -V|-Version
	       Print the tools version number and exit.

       -k|-keyword FILE
	       Include a keyword file that contains one keyword per line.  The
	       tool will read the keyword file line-by-line, and then compare
	       each line in the CSV file against each of those keywords.  The
	       tool will only print out those lines that match the keywords.

	       The words inside the keyword list are case insensitive.

       -w|-whitelist FILE
	       Include a keyword file that contains one keyword per line. The
	       file has the same format as the keyword file, and does the same
	       thing, except that this file lists up keywords of words that
	       should not be contained in the timeline. That is to say, this
	       file defines the "known good" or whitelisted lines that should
	       be kept out of the timeline.

	       The tool starts by comparing the known keywords before
	       processing the whitelist, meaning that keywords are first
	       filtered out before the whitelist is processed. So the
	       whitelist can be used in conjunction to the blacklist to narrow
	       down the scope even more.

	       It can also be used to remove known "good entries" or entries
	       that are not relevant to the current investigation out of the
	       timeline.

       -s|-scatter FILE
	       This only makes sense when the timeline contains records from
	       the MFT parser (NTFS filesystem). Then the tool will take the
	       creation time of each file that resides in the WINDOWS/System32
	       directory and scatter plot it against the MFT number of that
	       file. The tool will both plot the $FN and $SI creation time of
	       the file.

	       This can be useful during malware investigations, to quickly
	       find files that might have been added to the system32 folder.
	       When the operating system in installed, and during patching
	       there are usually several files written to the system32 folder
	       at once and since MFT's are associated sequentially there
	       should be clear association between MFT numbers and creation
	       time. However a typical malware does not create several files
	       in the system32 directory, a typical malware tries to hide and
	       does so by creating as few files as possible. That makes it
	       possible to view a scatter plot, showing the relationship
	       between creation time and MFT numbers to quickly spot those
	       outliers or anomalies. This technique can therefore be used for
	       data reduction.

	       This option creates a simple gnuplot data file and a gnuplot
	       script that can be used to create a simple scatter plot to see
	       those outliers. It will also make an attempt at identifying
	       those outliers with a simple algorithm. By default the tool
	       treats the entire dataset as a single slice and tries to find
	       the obvious outliers, however that behaviour can be changed
	       using the -m or --multi option to tell the tool to try to split
	       the dataset into slices.

	       The FILE portion should be the name of the output file the tool
	       writes to, it should only contain ASCII letters: a-z, A-Z,
	       underscore (_) and numbers 0-9, no dot.	The files created will
	       be: FILE.dat and FILE.cmd

	       Then the tool gnuplot has to be run, like:

	       gnuplot FILE.cmd

	       Which will produce a file called FILE.png, containing the
	       scatter plot.

	       If the tool detects any outliers in the dataset then the file
	       FILE_outliers.txt will be created. That file will contain a
	       list of all those files that the tool detected as outliers.

       -m|--multi
	       This option is only available when used with the -s FILE, to
	       create scatter plot of the creation time vs. $MFT entry
	       numbers. By default the tool treats the entire dataset as a
	       single slice and tries to detect outliers in it. Since the
	       relationship between $MFT entry numbers and creation time isn't
	       a simple line, in reality it consists of several straight
	       lines, there will be many false negatives when treating the
	       dataset as a single slice. Therefore the option of trying to
	       split the dataset into multiple smaller slices, and calculating
	       the outliers for each one of those has been provided.

	       This is a simple approach to this problem, and by no means
	       solves the issue at hand. This method does produce lots of
	       false positives (and it could also miss some, or produce false
	       negatives). However it will catch many of the items that get
	       missed by the first attempt.

	       Perhaps the best approach is to start with the default
	       behaviour of the tool, examine the graph manually. And if there
	       are some outliers in the dataset that are perhaps aligned with
	       another line, yet are obvious outliers, then to re-run the tool
	       using this option to try to see if it gets detected.

       -h|-help
	       Print this help message

       [DATE_RANGE]
	       The date range is formulated as one of the following:

	       MM-DD-YYYY      All dates from the date supplied date and
			       forward from them.  That is to say, the date
			       defines the starting date and all dates after
			       that date will be part of the selection.

	       MM-DD-YYYY..MM-DD-YYYY
			       This is a range, so all events that fall within
			       the boundaries set by these two dates will be
			       part of the selection.

DESCRIPTION
       l2t_process takes as an input the CSV output produced from the CSV
       output module of log2timeline and sorts the file.  It also has the
       capability to only let the file contain entries from a certain date
       range, or a similar behavior of the tool mactime from the SleuthKit
       (which works on mactime body files).

       The tool also removes any duplicate entries that might appear in the
       timeline. This can occur when recursive scans are made, since the same
       timestamp can be present in more than one file, such as registry
       entries both in NTUSER.DAT and inside various restore points.  The tool
       will remove the duplicate entries and change the filename so it
       includes all the files that the timestamp is found in.

       There is also "timestomp" detection, in the sense that if you used the
       MFT module of log2timeline to parse the NTFS $MFT file, and there are
       entries that have zero nanoseconds (second precision), which might be
       an indication of timestomping (since those tools only work on 32-bits
       of the timestamp, that is up to the second).  So if you run the tool
       with limited date range, and there are entries that fall outside the
       date entry that have zero nanoseonds (second precision), the tool will
       ask if you would like to include them in the timeline.

EXAMPLES
       Process the file combined.txt and only include entries that occured
       from January the 1st, 2004,  until March the 31st the same year.

       l2t_process -b combined.txt -y 2004-01-01..2004-03-31  >	 examine.txt

       Go over the file combined.txt and only include lines that fit the
       keyword list provided in the file dirty.txt

       l2t_process -k dirty.txt -b combined.txt > dirty.txt

AUTHOR
       Kristinn Gudjonsson <kristinn (a t) log2timeline ( d o t ) net> is the
       original author of the program.

       The tool is released under GPL so anyone can contribute to the tool.

COPYRIGHT AND LICENSE
       Copyright 2009-2011 by Kristinn Gudjonsson (kristinn ( a t )
       log2timeline ( d o t ) net )

       log2timeline is free software: you can redistribute it and/or modify it
       under the terms of the GNU General Public License as published by the
       Free Software Foundation, either version 3 of the License, or (at your
       option) any later version.

       log2timeline is distributed in the hope that it will be useful, but
       WITHOUT ANY WARRANTY; without even the implied warranty of
       MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
       General Public License for more details.

       You should have received a copy of the GNU General Public License along
       with log2timeline.  If not, see <http://www.gnu.org/licenses/>.

perl v5.20.2			  2012-05-22			L2T_PROCESS(1)
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