/RLEAPP

Returns Logs Events And Properties Parser

Primary LanguagePythonMIT LicenseMIT

RLEAPP

Returns Logs Events And Protobuf Parser

If you want to contribute hit me up on twitter: https://twitter.com/AlexisBrignoni

Requirements

Python 3.9 or above (older versions of 3.x will also work with the exception of one or two modules)

Dependencies

Dependencies for your python environment are listed in requirements.txt. Install them using the below command. Ensure the py part is correct for your environment, eg py, python, or python3, etc.

py -m pip install -r requirements.txt
or
pip3 install -r requirements.txt

To run on Linux, you will also need to install tkinter separately like so:

sudo apt-get install python3-tk

To install dependencies offline Troy Schnack has a neat process here: https://twitter.com/TroySchnack/status/1266085323651444736?s=19

Usage

CLI

$ python rleapp.py -t <zip | tar | fs | gz> -i <path_to_extraction> -o <path_for_report_output>

GUI

$ python rleappGUI.py 

Help

$ python rleapp.py --help

Contributing artifact plugins

Each plugin is a Python source file which should be added to the scripts/artifacts folder which will be loaded dynamically each time ALEAPP is run.

The plugin source file must contain a dictionary named __artifacts__ which defines the artifacts which the plugin processes. The keys in the __artifacts__ dictionary should be IDs for the artifact(s) which must be unique within ALEAPP; the values should be tuples containing 3 items: the category of the artifact as a string; a tuple of strings containing glob search patterns to match the path of the data that the plugin expects for the artifact; and the function which is the entry point for the artifact's processing (more on this shortly).

For example:

__artifacts__ = {
    "cool_artifact_1": (
        "Really cool artifacts",
        ('*/com.android.cooldata/databases/database*.db'),
        get_cool_data1),
    "cool_artifact_2": (
        "Really cool artifacts",
        ('*/com.android.cooldata/files/cool.xml'),
        get_cool_data2)
}

The functions referenced as entry points in the __artifacts__ dictionary must take the following arguments:

  • An iterable of the files found which are to be processed (as strings)
  • The path of ALEAPP's output folder(as a string)
  • The seeker (of type FileSeekerBase) which found the files
  • A Boolean value indicating whether or not the plugin is expected to wrap text

For example:

def get_cool_data1(files_found, report_folder, seeker, wrap_text):
    pass  # do processing here

Plugins are generally expected to provide output in ALEAPP's HTML output format, TSV, and optionally submit records to the timeline. Functions for generating this output can be found in the artifact_report and ilapfuncs modules. At a high level, an example might resemble:

import datetime
from scripts.artifact_report import ArtifactHtmlReport
import scripts.ilapfuncs

def get_cool_data1(files_found, report_folder, seeker, wrap_text):
    # let's pretend we actually got this data from somewhere:
    rows = [
     (datetime.datetime.now(), "Cool data col 1, value 1", "Cool data col 1, value 2", "Cool data col 1, value 3"),
     (datetime.datetime.now(), "Cool data col 2, value 1", "Cool data col 2, value 2", "Cool data col 2, value 3"),
    ]
    
    headers = ["Timestamp", "Data 1", "Data 2", "Data 3"]
    
    # HTML output:
    report = ArtifactHtmlReport("Cool stuff")
    report_name = "Cool DFIR Data"
    report.start_artifact_report(report_folder, report_name)
    report.add_script()
    report.write_artifact_data_table(headers, rows, files_found[0])  # assuming only the first file was processed
    report.end_artifact_report()
    
    # TSV output:
    scripts.ilapfuncs.tsv(report_folder, headers, rows, report_name, files_found[0])  # assuming first file only
    
    # Timeline:
    scripts.ilapfuncs.timeline(report_folder, report_name, rows, headers)


__artifacts__ = {
    "cool_artifact_1": (
        "Really cool artifacts",
        ('*/com.android.cooldata/databases/database*.db'),
        get_cool_data1)
}

Acknowledgements

This tool is the result of a collaborative effort of many people in the DFIR community.