Fake-Shop Detector API

About / Synopsis

• central source for serving browser plugin
• homogeneously integrates multiple local or api providers of trustworthy and known fraudulent sites
• provides mal2-model prediction capabilities
• caching and load balancing
• Project status: working/prototype

Table of contents

• Requirements
• Installation
• Usage
• Running with Docker
• REST-API
• About MAL2
• Contact
• License

Requirements

• Ubuntu 16.04
• Python 3.8
• PostgreSQL 10
• Python-Packages as defined in requirments.txt

Installation

Create a Python virtual environment with e.g. virtualenvwrapper or anaconda. The Python version used is 3.8.

$ mkvirtualenv -p /path/to/python3.8 rest-api

Install the required Python packages

pip install -r backend-api-server/requirements.txt

PostgreSQL 10 is used as database. Create a database and change the backend-api-server/swagger_server/mals/db/handler/db_handler.py accordingly.

The database initializiation is done by calling init-mal2-db.sh

#!/bin/bash
set -e
psql -v ON_ERROR_STOP=1 --username "$POSTGRES_USER" --dbname "$POSTGRES_DB" <<-EOSQL
    CREATE ROLE mal2user WITH LOGIN PASSWORD 'change_pass' SUPERUSER INHERIT CREATEDB CREATEROLE;
    CREATE DATABASE mal2restdb OWNER mal2user;
EOSQL

Usage

To run the server, please execute the following from the backend-api-server directory:

python3 -m swagger_server

and point your browser to:

http://localhost:8080/malzwei/ecommerce/1.1/ui/

Your Swagger definition lives here:

http://localhost:8080/malzwei/ecommerce/1.1/swagger.json

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

Running with Docker

For a full local deployment with an external db launch the docker build:

# starting the containers
docker-compose -f docker-compose.local_dev.yml up

For a full server deployment launch the docker build:

# building the image
docker-compose build --build-arg ENDPOINT_BASE=your.server.location

# starting the containers
docker-compose up

All details on the docker stand-alon or docker-compose build are provided in the files docker-compose.yml docker-compose.local_dev.yml and backend-api-server/docker/Dockerfile

The code as is contains settings for local dev deployment. The Dockerfile uses 'sed' to provide the production configuration and docker-compose up to start the integrated system RUN sed -i "s|127.0.0.1|$env_ENDPOINT_BASE|g" swagger_server/swagger/swagger.yaml

REST-API

The REST API documentation is available at http://localhost:8081/malzwei/ecommerce/1.1/ui/.

Please note all port configurations of the server are defined within the docker-compose build stage. Therefore make sure when adjusting the ports within docker-compose.yml to have identical ports for container and host within the mal2-rest-api service as they are passed to connexion[swagger-ui] at build time.

About MAL2

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

Contact

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 andrew.lindley@ait.ac.at | www.ait.ac.at or Woflgang Eibner, X-NET Services GmbH, we@x-net.at

License

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'