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The proliferation of Unmanned Aerial Vehicles (UAVs) raises a host of new security concerns. Our research resulted in a prototype UAV monitoring system, which captures flight data and performs real-time estimation/tracking of airframe and controller parameters utilizing the Recursive Least Squares Method. Subjected to statistical validation and trend analysis, parameter estimates are instrumental for the detection of some classes of cyber attacks and incipient hardware failures that can invariably jeopardize mission success. Our results demonstrate that achieving efficient anomaly detection during flight is possible through the intelligent application of statistical methods to system behavioral profiling.

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