Upon their arrival, Unmanned Autonomous Systems (UAS) brought with them many benefits for those involved in a military campaign. They can use such systems to reconnoiter dangerous areas, provide 24-hr aerial security surveillance for force protection purposes or even attack enemy targets all the while avoiding friendly human losses in the process. Unfortunately, these platforms also carry the inherent risk of being built on innately vulnerable cybernetic systems. From software which can be tampered with to either steal data, damage or even outright steal the aircraft, to the data networks used for communications which can be jammed or even eavesdropped on to gain access to sensible information. All this has the potential to turn the benefits of UAS into liabilities and although the last decade has seen great advances in the development of protection and countermeasures against the described threats and beyond the risk still endures.With this in mind the present work will describe a monitoring system whose purpose is to monitor UAS mission profile implementation at both high level mission execution and at lower level software code operation to tackle the specific threats of malicious code and possible spurious commands received over the vehicle's data links. All this is achieved by using intelligent and machine learning algorithms to monitor that the system's behavior matches or follows the overall concept of the mission, whilst also verifying that each software module is not engaging in apparent legitimate operation, but which could be the result of malicious code attempting to negatively affect the aircraft.This paper will describe the algorithms employed together with the architectural concept to later discuss simulation results where the advantages of the proposed monitoring system will be elaborated upon.