Abstract: Mechatronic systems (MES) have been widely studied and integrated into current smart engineering systems like robots, and control systems among others due to advance in technology. These systems are widely intruded on during operation through sensor attacks and their associated drawbacks. A robust technique for identifying and preventing sensor attacks in systems such as drones must be implemented in smart transportation networks. This paper proposes a novel intrusion anomaly detection approach (IADA) for MES sensors using recurrent neural networks. F1-score and One class classification (CM) anomaly detection was used to carry out performance assessment on several countermodels and classifiers. The results demonstrated that the proposed detection achieved 96% of F1-score, 99% of sensitivity, and 92% of precision in comparison to other counterparts across several drone platforms. The future research direction of the proposed model is also depicted.