Safeguarding the skies: the rise of machine learning approaches for cyberattack detection in unmanned aerial systems

Abstract Unmanned Aerial Systems (UAS) have become increasingly integral to industries such as agriculture, surveillance, transportation, and entertainment, raising significant concerns about their vulnerability to cyberthreats. Concurrently, cyberattacks have grown drastically in both frequency and complexity. In response, researchers have increasingly turned to Machine Learning (ML) techniques to improve UAS security, though accurately detecting attacks with low computational demands remains a