The proliferation of Unmanned Aerial Vehicles (UAVs) brings about many new security concerns. A common concern with UAV security is for an intruder to take control of a UAV, which leads for a need for a real time anomaly detection system. This research resulted in a prototype UAV monitoring system that captures flight data, and then performs real time estimation and tracking of the airframe and controller parameters. The aforementioned is done by utilizing the Recursive Least Squares Method (RLSM). Using statistical validation and trend analysis, parameter estimates are critical for the detection of cyber attacks and incipient hardware failures that can invariably jeopardize mission success. The results demonstrate that achieving efficient anomaly detection during flight is possible through the application of statistical methods to profile system behavior. The anomaly detection system that was designed can be performed in real time while the UAV is in flight, constantly verifying its parameters.