downloads APK files from Google Playstore based on the package signatures received from the mal2 android app to pass sources further on to to the mal2_ai predictive backend
The APK downloader is implemented in Python and is used to download APK files from the Google Playstore based on the Android package signatures received by the mal2 Android-Malware Analysis App to further pass them on to the mal2_ai predictive backend. As soon as the check process is been initiated by the user, the app starts uploading the signatures one after the other. As soon as a signature arrives at the apk downloader API endpoint, the file is downloaded using “Google play cli” and passed on to the malware detection server, which once the analysis has completed, returns with the in depth and explainable results to render in the app.
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 andrew.lindley@ait.ac.at | www.ait.ac.at or Woflgang Eibner, X-NET Services GmbH, we@x-net.at
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'