Detecting Periodicity in Serial Data through Visualization

Detecting suspicious or malicious user behavior in large networks is an essential task for administrators which requires significant effort due to the huge amount of log data to be processed. However, several of these activities can be rapidly identified since they usually demonstrate periodic behavior. For instance, periodic activities by specific users accessing the billing system of a financial institution may conceal fraud. Detecting periodicity in user behavior not only offers security to the network, but may prevent future malicious activities. In this paper, we present visualization techniques that aim to detect authorized (or unauthorized) user activities that seem to appear at regular time intervals.