Our “BATSense” security event detection methodology has been running at Michigan State University’s campus for a year and is successfully detecting security anomalies across 300k devices. In this presentation, we will describe the use machine learning, specifically the TBATS forecasting algorithm, to predict future trends for the number of events per second for a variety of device types. The forecasted values are compared against actual observations to alert security personnel of significant deviations. Anomalies are detected based on logs relevant to security events; they may be system modifications, system failures or a football game. Forecasts are never perfect, but when measured over extended use, we have shown that false positives are manageable (1 per week) for true positives of 1 per day. The result a methodology that has been developed and tweaked over time to effectively detect security events, and lessons learned over a year. All arguments presented in this talk will be backed by real world (anonymized) data collected at our university shared with the audience.