Representations of data for repetitive user behaviors using Markov Chains

This report presents problematic issues with analyzing user behaviors of a repetitive nature for Anomaly Detection using the Markov Chain model. The users in the data tend to use certain events in succession for a long period of time. Doing the same thing in succession can be normal but when can these users be considered to have an abnormal behavior? The work done in this report presents two alterative ways of representing the data for letting the Markov Chain capture large sections of repeating events without needing to increase the order of the Markov Chain. The presented representations show promising results for an increase in the Markov Chain capability to distinguish users of repetitive nature from each other, as well as suggestions for future development