Description: Python Application to get Forensic Artifacts from macOS. Information on Forensic Artifacts used from [1] (Brandt M., 2017) Relies on python standard library, so no external libraries are required.
License: MIT
Status:
Use @ your own risk, since this application is still in ALPHA no extensive testing has been done (yet). If you want to help see Contributions section.
The script gathers evidence from categories such as Installed Applications, Network Settings, Printers, System Information User Accounts, Keychains, Firewall, Launch Agents and Launch Daemons etc. Not all of them work for all of the versions of macOS, I am working on implementing some more robust checking of macOS Versions and relative artifacts.
- Set verbosity of log file -v --verbose
- Collect and parse browser artefacts -b --browser *Currently only safari
- Specify output directory -o --output-path
- Specify starting directory -s --start *directory from which to start collecting fileinformation
- To hash or not to hash -H --hash
- Provide a list of directories to skip during collection -w --whitelist
Example:
python3 nidaba.py -s / -o /Volumes/collector/
The OS X specific artifacts that will be collected (in the future) are based on
- Hacking MacIntosh [1] and,
- OSX Incident Response [2]
The following table keeps track of the artifacts covered:
See database.db for detailed list of artifacts.
Created collector script to make a file listing based on OS X Incident Response [2]
- Clone Repository to your local machine
- Copy the repository to a USB Stick
- Connect USB Stick to the target device and,
- Open a terminal at the location of the script e.g.
/Volumes/<USBSTICK> - Run nidaba.py with sudo rights
sudo python3 nidaba.py
Note Tool needs Full Disk access to be able to gather the artefacts.
Nidaba is developed with Python 3, with macOS 12.13 Apple is no longer bundling Python 2.7, see https://developer.apple.com/documentation/macos-release-notes/macos-12_3-release-notes#Python
Contributors are welcome, please contact me if you like to help in the development. Either on GitHub or via my Twitter handle @_APTwi
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[1] Brandt, M. (2017). Mac OS Hacking: Professionelle Werkzeuge und Methoden zur forensischen Analyse des Apple-Betriebssystems. Haar: Franzis Verlag.
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[2] Bradley, Jaron. OS X Incident Response (p. 76). Elsevier Science. Kindle Edition.
https://github.com/Yelp/osxcollector/blob/master/osxcollector/osxcollector.py
https://github.com/mac4n6/macMRU-Parser/blob/master/macMRU.py