Project Description
Idea & Motivation
MAL2 (MAchine Learning detection of MALicious content) will apply Deep Neural Networks and Unsupervised Learning to advance cybercrime prevention by a) automating the discovery of fraudulent eCommerce and b) evaluating the capabilities of detecting Potentially Harmful Apps (PHAs) in Android operating systems. Online shopping is commonplace, with 61.6% of Austrians already using this form of commerce. Ripping of customers through fraudulent eCommerce shops is a rapidly growing area in cybercrime. Exposing such fake offerings however is a labour intensive, manual task as often, dozens or more of these copies exist at the same time - every week more than 150 new fake online-shops are entered for manual verification. MAL2 provides means for advancing the automation and detection of fake-shop cyberquatting through machine learning technologies by classifying sites based on their structural similarity.
Aim & Implementation
The goal of the project is to train a Neural Network to evaluate the discoverability and explainability of upcoming attack patterns. The implementation of this aim is (i) to release Open Source framework which provides integrated functionality along the required pipeline - from data extraction, feature composition up to Neural Network training and analysis of results (ii) to execute its components at large-scale within Hadoop and GPU cluster support and (iii) to publish the harvested Ground-Truth dataset, the extracted features as well as the trained Neural Network in both application domains on open data platforms.
Results & Publication
To visualize the projects results and to raise awareness for cybercrime prevention in the general public, two demonstrators are deployed at Watchlist Internet that allow live-inspection on the trustworthiness of eCommerce sites and Android Apps.