Automagically extract forensic timeline from volatile memory dumps.

Requirements

• Python 3
• Volatility
• mactime (from SleuthKit)

(Developed and tested on Debian 9.6 with Volatility 2.6-1 and sleuthkit 4.4.0-5)

How it works

AutoTimeline automates this workflow:

• Identify correct volatility profile for the memory image.
• Runs the timeliner plugin against volatile memory dump using volatility.
• Runs the mftparser volatility plugin, in order to extract $MFT from memory and generate a bodyfile.
• Runs the shellbags volatility plugin in order to generate a bodyfile of the user activity. (suggested by Matteo Cantoni).
• Merges the timeliner, mftparser and shellbags output files into a single bodyfile.
• Sorts and filters the bodyfile using mactime and exports data as CSV.

Installation

Simply clone the GitHub repository:

git clone https://github.com/andreafortuna/autotimeliner.git

Usage

autotimeline.py [-h] -f IMAGEFILE [-t TIMEFRAME] [-p CUSTOMPROFILE]

optional arguments:
  -h, --help            show this help message and exit
  -f IMAGEFILE, --imagefile IMAGEFILE
                        Memory dump file
  -t TIMEFRAME, --timeframe TIMEFRAME
                        Timeframe used to filter the timeline (YYYY-MM-DD
                        ..YYYY-MM-DD)
  -p CUSTOMPROFILE, --customprofile CUSTOMPROFILE
                        Jump image identification and use a custom memory
                        profile

Examples

Extract timeline from TargetServerMemory.raw, limited to a timeframe from 2018-10-17 to 2018-10-21:

./autotimeline.py -f TargetServerMemory.raw -t 2018-10-17..2018-10-21

Extract timeline from all images in current directory, limited to a timeframe from 2018-10-17 to 2018-10-21:

./autotimeline.py -f ./*.raw -t 2018-10-17..2018-10-21

Extract timeline from TargetServerMemory.raw, using a custom memory profile:

./autotimeline.py -f TargetServerMemory.raw -p Win2008R2SP1x64

All timelines will be saved as $ORIGINALFILENAME-timeline.csv.

TODO

• Better image identification
• Better error trapping