• Management for manually classifying reported websites (e.g. via fake shop detection REST API).
• Project status: working/prototype

• Requirements
• Installation
• Usage
• Screenshots
• REST-API
• Resources (Documentation and other links)
• License
• About MAL2

• Ubuntu 18.04
• Python 3.5
• PostgreSQL 10
• Python-Packages as defined in requirments.txt

Create a Python virtual environment with e.g. virtualenvwrapper. The Python version used is 3.5.

$ mkvirtualenv -p /path/to/python3.5 mal2DB

Install the required Python packages

pip install -r db/requirements.txt

PostgreSQL 10 is used as database. Create a database and change the db/settings/base.py accordingly.

DB_HOST = "localhost"
DB_USER = "mal2db"
DB_NAME = "mal2db"
DB_PASSWORD = "MY_SECRET_PASSWORD"

After that, the database is initialized as follows:

cd db
./manage.py migrate
./manage.py loaddata init_groups
./manage.py loaddata init_website_risc_score
./manage.py loaddata init_website_types
./manage.py loaddata init_website_category
./manage.py loaddata init_websites_reported_by

The admin user is created with the following command:

$ ./manage.py createsuperuser

Enter a mail server in the db/settings/base.py.

ADMINS = (
    ("Name", "[email protected]"),
)

SERVER_EMAIL = "[email protected]"

EMAIL_HOST = "mail.domain.tld"
EMAIL_HOST_USER = "mal2DB"
EMAIL_HOST_PASSWORD = "MY_SECRET_PASSWORD"

DEFAULT_FROM_EMAIL = "[email protected]"

To create web page screenshot the chromium-chromedriver is needed.

$ apt install chromium-chromedriver

Now the server can be started with the Django command runserver.

./manage.py runserver

In your browser, you can now go to http://127.0.0.1:8000/.

Attention, this is for development only. In a production environment, for example, uwsgi can be used with apache2.

The REST API documentation is available at http://127.0.0.1:8000/api.

• Django Web Framework
• Django REST Framework

The MAL2 project applies Deep Neural Networks and Unsupervised Machine Learning to advance cybercrime prevention by a) automating the discovery of fraudulent eCommerce and b) detecting Potentially Harmful Apps (PHAs) in Android. The goal of the MAL2 project is to provide (i) an Open Source framework and expert tools with integrated functionality along the required pipeline – from malicious data archiving, feature selection and extraction, training of Machine Learning classification and detection models towards explainability in the analysis of results (ii) to execute its components at scale and (iii) to publish an annotated Ground-Truth dataset in both application domains. To raise awareness for cybercrime prevention in the general public, two demonstrators, a Fake-Shop Detection Browser Plugin as well as a Android Malware Detection Android app are released that allow live-inspection and AI based predictions on the trustworthiness of eCommerce sites and Android apps.

The work is based on results carried out in the research project MAL2 project, which was partially funded by the Austrian Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK) through the ICT of the future research program (6th call) managed by the Austrian federal funding agency (FFG).

• Austrian Institute of Technology GmbH, Center for Digital Safety and Security AIT
• Austrian Institute for Applied Telecommunications ÖIAT
• X-NET Services GmbH XNET
• Kuratorium sicheres Österreich KSÖ
• IKARUS Security Software IKARUS

More information is available at www.malzwei.at

For details on behalf of the MAL2 consortium contact: Andrew Lindley (project lead) Research Engineer, Data Science & Artificial Intelligence Center for Digital Safety and Security, AIT Austrian Institute of Technology GmbH Giefinggasse 4 | 1210 Vienna | Austria T 43 50550-4272 | M 43 664 8157848 | F 43 50550-4150 [email protected] | www.ait.ac.at or Woflgang Eibner, X-NET Services GmbH, [email protected]

The MAL2 Software stack is dual-licensed under commercial and open source licenses. The Software in this repository is subject of the terms and conditions defined in file 'LICENSE.md'