  Investigating efficiently the data collected from a system's activity can
help to detect malicious attempts and better understand the context behind past
incident occurrences. Nowadays, several solutions can be used to monitor system
activities to detect probable abnormalities and malfunctions. However, most of
these systems overwhelm their users with vast amounts of information, making it
harder for them to perceive incident occurrences and their context. Our
approach combines a dynamic and intuitive user interface with Machine Learning
forecasts to provide an intelligent investigation tool that facilitates the
security operator's work. Our system can also act as an enhanced and fully
automated decision support mechanism that provides suggestions about possible
incident occurrences.
