Descripció del projecte
Anomaly detection has long been a question of great interest in a wide range of domains such as networks systems, security, biomedical… It is a classical problem that can be applied to any area seeking to detect unexpected events, which differ from the norm. It is often applied on unlabeled data which is known as unsupervised anomaly detection.
This project aims to develop deep learning methods for anomaly detection applied to network signaling data, among other fields. The goal is to go beyond the state-of-the-art in the use of machine learning algorithms and GPU computing, exploring generative models such as normalizing-flows. By working in this project, the PhD student will have access to powerful computational resources as well as a high amount of data.
