ALEAPP CLI abrignoni
winget install --id=abrignoni.ALEAPP-cli -e
Android Logs Events And Protobuf Parser
ALEAPP CLI is a Python-based tool designed to analyze Android logs, events, and protobuf data. It provides forensic investigators and security analysts with a comprehensive solution to extract, parse, and report on Android artifacts, enabling deeper insights into device activity and user behavior.
Key Features:
- Cross-Platform Support: Runs on multiple operating systems, including Windows, Linux, and macOS.
- Flexible Input Handling: Processes input in various formats, such as ZIP, TAR, GZ, and raw file system images.
- Artifact Extraction: Automatically identifies and processes a wide range of Android artifacts using customizable plugins.
- Report Generation: Produces detailed reports in HTML, TSV, and timeline formats for easy analysis and sharing.
- Customizable Plugins: Allows users to extend functionality by adding new artifact parsers or modifying existing ones.
Audience & Benefit:
Ideal for forensic investigators, digital forensics professionals, and security analysts working with Android devices. ALEAPP CLI streamlines the process of extracting and analyzing Android artifacts, saving time and improving efficiency in incident response, forensic examinations, and threat analysis. Its ability to handle complex data formats and generate structured reports makes it a valuable tool for both experienced investigators and those less familiar with Android forensics.
Installable via winget, ALEAPP CLI is designed for flexibility and ease of use, making it accessible to a wide range of users in the digital forensic community.
README
Android Logs Events And Protobuf Parser
If you want to contribute hit me up on twitter: https://twitter.com/AlexisBrignoni
Details in blog post here: https://abrignoni.blogspot.com/2020/02/aleapp-android-logs-events-and-protobuf.html
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
Compile to executable
To compile to an executable so you can run this on a system without python installed.
To create aleapp.exe, run:
pyinstaller --onefile aleapp.spec
To create aleappGUI.exe, run:
pyinstaller --onefile --noconsole aleappGUI.spec
Usage
CLI
$ python aleapp.py -t -i -o
GUI
$ python aleappGUI.py
Help
$ python aleapp.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_v2__
at the very beginning of the module, which defines the artifacts that the plugin processes. The keys in the __artifacts_v2__
dictionary should be IDs for the artifact(s) which must be unique within ALEAPP. The values should be dictionaries containing the following keys:
name
: The name of the artifact as a string.description
: A description of the artifact as a string.author
: The author of the plugin as a string.version
: The version of the artifact as a string.date
: The date of the last update to the artifact as a string.requirements
: Any requirements for processing the artifact as a string.category
: The category of the artifact as a string.notes
: Any additional notes as a string.paths
: A tuple of strings containing glob search patterns to match the path of the data that the plugin expects for the artifact.function
: The name of the function which is the entry point for the artifact's processing as a string.
For example:
__artifacts_v2__ = {
"cool_artifact_1": {
"name": "Cool Artifact 1",
"description": "Extracts cool data from database files",
"author": "@username",
"version": "0.1",
"date": "2022-10-25",
"requirements": "none",
"category": "Really cool artifacts",
"notes": "",
"paths": ('*/com.android.cooldata/databases/database*.db',),
"function": "get_cool_data1"
},
"cool_artifact_2": {
"name": "Cool Artifact 2",
"description": "Extracts cool data from XML files",
"author": "@username",
"version": "0.1",
"date": "2022-10-25",
"requirements": "none",
"category": "Really cool artifacts",
"notes": "",
"paths": ('*/com.android.cooldata/files/cool.xml',),
"function": "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:
__artifacts_v2__ = {
"cool_artifact_1": {
"name": "Cool Artifact 1",
"description": "Extracts cool data from database files",
"author": "@username", # Replace with the actual author's username or name
"version": "0.1", # Version number
"date": "2022-10-25", # Date of the latest version
"requirements": "none",
"category": "Really cool artifacts",
"notes": "",
"paths": ('*/com.android.cooldata/databases/database*.db',),
"function": "get_cool_data1"
}
}
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)
Acknowledgements
This tool is the result of a collaborative effort of many people in the DFIR community.