A Scalable Ensemble-based Framework to Analyse Users' Digital Footprints for Cybersecurity

In the field of cybersecurity, it is of great interest to analyse user logs in order to prevent data breach issues caused by user behaviour (human factor). A scalable framework based on the Elastic Stack (ELK) to process and store log data coming from digital footprints of different users and from applications is proposed. The system exploits the scalable architecture of ELK by running on top of a Kubernetes platform, and adopts ensemble-based machine learning algorithms to classify user behaviour and to eventually detect anomalies in behaviour.