Andrés Gómez Ramirez
PhD : Frankfurt U. : 2018
The use of distributed computational resources for the solution of scientific problems, which require highly intensive data processing is a fundamental mechanism for modern scientific collaborations.
The Worldwide Large Hadron Collider Computing Grid (WLCG) is one of the most important examples of a distributed infrastructure for scientific projects and is one of the pioneering examples of grid computing.
The WLCG is the global grid that analyzes data from the Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN), with 170 sites in 40 countries and more than 600,000 processing cores.
Our CEO, Andrés Ramirez, Phd Arhuaco, a tool developed as a doctoral project at the Goethe University of Frankfurt, has several proposals:
- It is focused on grid computing which can be extended to virtualized or cloud services.
- It uses Linux containers to isolate grid jobs and then extract jobs’ behavior information.
- Arhuaco utilizes Deep Neural Networks for the classification of grid jobs as normal or malicious, on execution and in real-time.
- It also provides the ability to generate complementary training data by Recurrent Neural Networks to improve the detection performance and adapt the detector to new environments.
Read “Deep Learning and Isolation Based Security for Intrusion Detection and Prevention in Grid Computing“
