An ensemble-based framework for user behaviour anomaly detection and classification for cybersecurity

Abstract

Nowadays, the speed of the user and application logs is so quick that it is almost impossible to analyse them in real time without using high-performance systems and platforms. In cybersecurity, human behaviour is responsible directly or indirectly for the most common attacks (i.e. ransomware and phishing). To monitor user behaviour, it is necessary to process fast user logs coming from different and heterogeneous sources, having part of the data or some entire sources missing. A framework based on the elastic stack (ELK) to process and store log data in real time from different users and applications is proposed for this aim. This system generates an ensemble of models to classify user behaviour and detect anomalies in real time, exploiting the advantages of the ELK-based software architecture and of the Kubernetes platform. In addition, a distributed evolutionary algorithm is used to classify the users by exploiting their digital footprints derived from many data sources. Experiments conducted on two real-life data sets verify the approach’s goodness in detecting anomalies in user behaviour, coping with missing data and lowering the number of false alarms.