A WIP forensics tool for Windows written in Python, for parsing Google Analytics cookies.
- Select the browser and browser version you're using from the dropdown at the top
- Find the cookies file using the instructions for your browser
- Click 'Process'
- You can view information for a given domain by selecting that domain with the Domain dropdown after loading a cookie file
- You can also export all Google Analytics cookies in a parsed format to .csv files in a chosen directory by clicking the 'Output to .csv' button
- Every command requires both an input file path (
-i
or--input
) and a browser name (-b
or--browser
) to be specified. Currently,-b
/--browser
can only befirefox.3+
orcsv
- The
info
command, which does not require any additional parameters, will show the number of GA cookies found and the number of unique domains for which any cookies were found
- The
list-domains
command, which does not require any additional parameters, will list all domains for which GA cookies were found
- The
domain-info
command, which requires the additional parameter-d
or--domain
, will list a parsed version of all available information for the given domain. The domain should be given in the format in which it is found withlist-domains
- The
export-csv
command, which requires the additional parameter-o
or--output
which should be a directory path of the output directory, will export all found cookie data to .csv files in the given directory. The-f
or--force-overwrite
option can be given to automatically overwrite files if they exist without prompting the user.
- Reading and parsing cookies.sqlite from Firefox v3+ and any browser from which you can retrieve cookies as a .csv file
- Analysing and parsing all relevant Google Analytics cookies (_ga, __utma, __utmb, __utmz)
- Presenting all available information for a given domain
- Exporting GA cookie information to a .csv file
GACP is currently only tested on Firefox v3+ and .csv, and as always any critical evidence should be double-checked by manually inspecting the relevant cookies
I would like to thank Kevin Ripa, for being such an excellent instructor and mentor, and providing the inspiration to create this tool.