Summary
Funder : INNOVIRIS (Brussels Region)
Start : 2015
End : 2018
Academic coordinator : Olivier Markowitch
Abstract
The aim of the BruFence project is to design systems based on machine learning and big data mining techniques that allow sensible and secure systems to automatically detect attacks and fraudulent behaviors.
Advanced persistent threat detection market as well as scalable fraud detection is expected to have a high progression rate in the next years, therefore the project is twofold and addresses researches on:
- Automatic detection of threats and attacks against communication systems, managed file transfer and collaboration platforms;
- Automatic detections of fraud in large amount of transactions.
During the compilation of a risks and threats analysis, it is not trivial for defenders to foresee all the potential risks of a communication system. Recent attacks highlight the limits of current threats models and risks analysis methodologies. The aim of our research is to enhance the security of communication systems by enabling automatic learning from past and current attack attempts. The system will also be designed to report and assist the managers of the communication platform.
In the framework of fraud detection, automatic systems are essential since it is not possible or easy for a human analyst to detect fraudulent patterns in transaction datasets, often characterized by a large number of samples, many dimensions and online update. The research undertaken in the framework of the BruFence project will propose new efficient techniques for automatic detections of fraud in large amount of transactions.