Drones have become increasingly popular in various domains, but
their vulnerability to cyber-attacks poses a significant threat to their
security and the safety of their operations. In this study, we propose a
comparative explainability analysis for machine learning-based tech niques for drones GPS spoofing attack detection. For this purpose,
we first generate our own dataset, then we implement several ma chine learning models for a binary classification. Finally, we analyze
and compare the results. We strongly believe that the findings of our
study will contribute to the development of effective countermeasures
and defense mechanisms against drone hacking incidents, ultimately
ensuring the integrity and reliability of drone-based systems
