The Automatic Dependent Surveillance-Broadcast (ADS-B) system is vital for modern air traffic control, enhancing both safety and efficiency. However, its lack of encryption and authentication introduces significant security risks. One major threat is the ADS-B spoofing attack, where attackers manipulate the ICAO address in ADS-B messages to create fake aircraft or impersonate trusted ones, potentially misleading pilots and air traffic controllers and leading to dangerous maneuvers. To address these challenges, we propose the Revin-Discrete Cosine Patch Attention (RDPA)—a novel method leveraging the RevIn mechanism, Discrete Cosine Transform, and patch mechanism within a Transformer architecture. This approach is specifically designed to detect spoofing attacks within ADS-B data that traditional analyses might overlook, including deceptive maneuvers mimicking normal flight behavior, sudden deviations in flight paths, and inconsistencies in transmitted data, all of which pose substantial risks to airspace security. Our RDPA model has been rigorously tested on large-scale ADS-B datasets, including simulated deceptive maneuvers across various flight stages such as departing, maneuvering, and landing. Experimental results show that RDPA excels in detecting spoofing attacks with high accuracy, achieving an F1-score of 99.09\%, significantly outperforming existing methods. This highlights RDPA’s potential as a crucial tool for enhancing air traffic security.