DDoS Attacks Detection with Deep Learning Model Using a Cloud Architecture

Conventional techniques for the detection of distributed denial-of- service attacks have proven to be insufficient in the face of the variety and mutation of the typology of these anomalous behaviors, likewise, emerging detection and prevention technologies are presented as solutions in workstations and/or in processes not deployed as services. Therefore, this project implements an Intrusion Detection System (IDS) supported by deep learning techniques, with a service-oriented architecture in the cloud for the detection of Distributed Denial of Service (DDoS) attacks, obtaining as a result the improvement of the classification metrics as well as the availability of these resources in an environment deployed as a service.