New Innovations in eIDAS-compliant Trust Services: Anomaly detection on log data

Abstract

TrustedX is a product that provides electronic signatures, it has been on the market for several years now, in result of that, market tendencies have changed and technologies too which made this product be on the same level as the competitors. This project tries to adapt an existing solution to the necessities companies have. To do so we proposed multiple changes, which have been divided into 2 parts, being the first one a part done by 3 UPC students (Arthur Bernal, Xiaolei Lin and Marc Méndez) with help of Dr. Francisco Jordán, the director of the project. The second one, being an individual part which will be only done by Marc Méndez. The common part is based on adding necessary technology, capabilities and functionalities that make this product able to work as a cloud multitenant system. This product will have the ability to sign official documents through the internet with the same validity as notarized signatures. The company had already the on-premise product developed, but now, instead of having an on-premise system we will create a new cloud-based multitenant system that has no location restrictions under the name of TrustedX as a Service (TXaaS). The whole TXaaS System is a common goal of this multi-part project. The specific part is the one that has strong relation to the specialization of the master, data science, in particular Deep Learning. Nowadays, a lot of products or companies use Machine Learning techniques to many different areas being marketing and business intelligence the two top areas of application. The proposal of this project is to develop a Deep Recurrent Neural Network-based Autoencoder for anomaly detection, which is able to learn large and difficult data patterns generated by the TXaaS.