## 3 Methodology
Figure 4 illustrates the structure of the developed GAN designed for executing the ATP task. As depicted in Figure 4, the GAN architecture consists of two primary components: the Generator (G) and the Discriminator (D) [35]. Random feasible trajectories following  
Figure 4: The structure of the developed GAN designed for executing the ATP task. a Gaussian distribution are fed into the feature extractor (\(z\)). The G-network generates samples resembling those within the database, while the D-network differentiates between the data generated by the Generator and the data from the trajectory database. The D-network undergoes training based on its inputs, and the Regressor predicts forthcoming trajectories by considering both original and adversarial samples. The detailed architecture of the proposed Generative Adversarial Networks aimed at Aircraft Trajectory Prediction (GAN-ATP) is shown in Figure 5, in which the trajectories' data generation is illustrated step by step [52]. According to Figure 5, generated trajectory data are applied to the Transformer. Simultaneously, the Latent Vector is normalized and fully propagated through the latent style vector. The output of the Latent Style Vector block is then passed to the Transformer via the affine \(A\) (\"adaptive instance normalization\") block. The mixture is transmitted from the Transformer into \(6\times 1\) convolution layers supported by weights \(w\). Gaussian noise is then added via Pre-channel Scaling, and Bias is added as well. New trajectory data are generated after normalization. One style of latent vector per generated trajectory is used during training, as it performs its stylization independently of other style blocks. Initialization and batch normalization procedures were executed prior to the training phase, and both the Generator (G) and Discriminator (D) networks were established using a full-propagation architecture. The Mean Square Error (MSE) served as the metric for performance assessment, and the error rates were employed to gauge the effectiveness of the Regressor. The GAN framework has the potential to enhance its ability against adversarial attacks through a more effective defense strategy when compared to the adversarial retraining technique. This advantage arises from the GAN's defense approach, which can effectively counter both white-box and black-box attacks, whereas adversarial retraining is limited to addressing white-box attacks exclusively. Predictably, the GAN remains vulnerable to adversarial attacks, inevitably allowing some adversarial samples to pass the Discriminator when the GAN is fooled, resulting in erroneous predictions by the regressor for future trajectories \(\vartheta\). In such instances, the application of Blockchain, a form of Distributed Ledger Technology (DLT), becomes relevant. It can be utilized to retrieve data registered in the preceding block [40]. Drawing upon the reliable and authenticated trajectory data, if a predicted trajectory surpasses a particular threshold, the prediction is considered unsuccessful. Consequently, the Blockchain ensures that inaccurately predicted trajectories are prevented from being recorded in the subsequent block [53]. In fact, its consensus protocol hinders the incorporation of the new block into the chain. To this end, Figure 6 shows the configuration of  
Figure 5: The designed GAN architecture for aircraft trajectory data generation. the Blockchain, trajectory storage (data within blocks), consensus determination, and the implementation of new blocks. As shown in Figure 6, the Generator \(G\) produces adversarial samples that are similar to those in the UAS-S4 trajectory database. Next, the Discriminator \(D\) discriminates between two inputs (those from Generator G and those from the UAS-S4 trajectory database) and trains the samples regardless of if they are original or adversarial. Then, the future trajectories are predicted by the repressor. Meanwhile, the original UAS-S4 trajectories are stored in the UAS-S4 database via the Blockchain.