Awesome
AutoTimeliner
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