## 1 Introduction
Utilizing the original dataset, the \"Generator\" learns to generate new datasets with similar statistics to the original data, while the \"Discriminator\" assesses these generated datasets [35]. This framework finds application in various contexts, particularly data reconstruction. The GAN can also generate adversarial samples, where the prediction model is trained using a provided dataset, while the Generator simultaneously produces adversarial samples that are being used in the training phase. This approach's strength lies in its capacity to defend against both white-box and black-box attacks. GAN algorithms have been successfully utilized to solve unmanned moving vehicle trajectory prediction problems. A GAN model was designed for a UAS with the aim of predicting the landing trajectories [37]. Such a model could be used to manage landing paths around an airport. A GAN model was also used in another research study for an UAV's long-term trajectory prediction [38]. The GAN algorithms have been used for marine and ground transportation to accurately predict trajectories [39]. In terms of reliability, GAN algorithms remain susceptible to black-box attacks. It is, therefore, essential to label and store the original values of predicted trajectories to improve data security and the GAN's performance. To improve the reliability of GAN algorithms (the first objective of our study), Blockchain was utilized as a Ledger Technology (LT) to record the predicted trajectory values [40] (the second objective of our study). Integrating Blockchain technology as a ledger for storing predicted trajectories offers robust advantages in data security, integrity, and resilience against adversarial attacks. The immutable nature of Blockchain ensures trajectories remain tamper-resistant, transparent, and traceable. Decentralization and cryptographic security further bolster trust in trajectory data. Consensus mechanisms prevent unauthorized alterations, maintaining data accuracy. This blockchain-based approach assures accountability, compliance, and data protection, making it a powerful solution for secure trajectory storage, particularly in scenarios where data integrity and resilience against adversarial threats are paramount [41]. This approach boosts the resilience of the GAN-ATP model against forthcoming adversarial attacks. Blockchain Ledger Technology (BLT) stores both the GAN-predicted trajectories, relying on previous block confirmations on the blockchain. The hash associated with the previous block and the predicted trajectories values are stored in the new block for forthcoming predictions. Note that the hash is a unique digital fingerprint that verifies the integrity and authenticity of data. For development of smart contracts associated with the BLT, formal methods play a pivotal role by using mathematical techniques to rigorously verify correctness, security, and reliability. They ensure that smart contracts adhere to intended behaviors, preventing costly errors and security breaches. Formal methods involve creating models, defining specifications, and employing verification tools to prove mathematically that smart contracts meet desired properties [42]. The benefits include early issue detection, heightened security, enhanced trust, and long-term reliability, making formal methods an essential practice for building robust and trustworthy blockchain applications [43]. This research study offers four main contributions. The first constitutes the design of a customized GAN architecture for trajectory prediction application to our UAS-S4. Expressing the adversarial attack concept in time-series problems and imposing adversarial attacks on the GAN-based UAV trajectory prediction are the second and third contributions. The fourth major contribution is the design of a blockchain-based ledger technology that stores original predicted trajectories and has a high level of robustness in the face of adversarial attacks. This article comprises five sections. Section 2 describes the aircraft trajectory prediction problem in detail.