## 5 Conclusions  
A method resilient to adversarial attacks was developed for predicting aircraft trajectories. A trajectory prediction model was designed based on a Generative Adversarial Network (GAN). Demonstrating its capability, the GAN effectively predicted forthcoming trajectories using the provided UAS-S4 trajectory database, encompassing vectors containing latitude, longitude, altitude, heading, speed, and time.  
The adversarial attack concept was explained, showcasing the GAN's ability for producing adversarial samples during its training process. The designed GAN was subjected to a comparison with the Long-Short Term Memory trajectory prediction model, evaluating their respective robustness against attacks. Both the LSTM and GAN models were evaluated, during which they were exposed to adversarial attacks founded on the Adapted Fast Gradient Sign Method (AFGSM). The resulting fooling rates validated that both models were vulnerable to adversarial samples generated through a white-box approach.  
In order to formulate effective defense strategies, algorithms such as Adversarial Retraining (AR), Generative Adversarial Networks, and Blockchain Ledger Technology (BLT) were developed. The GAN-BLT algorithm that was ultimately designed showed the highest performance compared to the three defense algorithms, in which it recorded a  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Iterations** & & **1** & **2** & **3** & **4** & **5** \\ \hline \multirow{2}{*}{Fooling Rate [\%]} & LSTM methodology & 29.4 & 41.7 & 48.2 & 54.1 & **59.9** \\ \cline{2-6}  & GAN methodology & 31.5 & 43.2 & 49.5 & 56.7 & **61.3** \\ \hline \hline \end{tabular}
\end{table}
Table 4: LSTM and GAN Model Performance under White-Box Attacks Generated by the AFGSM.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Iterations** & & **1** & **2** & **3** & **4** & **5** \\ \hline \multirow{3}{*}{Fooling Rate [\%]} & AR-LSTM & 25.4 & 14.7 & 11.1 & 10.3 & **9.8** \\ \cline{2-6}  & GAN & 27.3 & 12.5 & 8.4 & 5.5 & **4.8** \\ \cline{2-6}  & GAN-BLT & 13.7 & 7.6 & 4.8 & 2.9 & **2.8** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of the Defense Mechanisms Adversarial Retraining, GAN, and Blockchain Undergoing Black-Box Attacks.  
-fooling rate of 2.8%. The GAN-BLT model exhibits the ability to excel beyond both GAN and AR-LSTM defense strategies when confronted with either white- or black-box attacks.  
Conceptualization, S.M.H.; Methodology, S.M.H. and S.A.H.; Software, S.M.H. and S.A.H.; Validation, S.M.H., R.M.B. and G.G.; Formal analysis, S.M.H., S.A.H. and G.G.; Investigation, S.M.H. and S.A.H.; Resources, R.M.B. and G.G.; Data curation, S.M.H., S.A.H. and G.G.; Writing--original draft, S.M.H.; Writing--review & editing, R.M.B. and G.G.; Supervision, R.M.B. and G.G.; Project administration, R.M.B.; Funding acquisition, R.M.B. and G.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by NSERC within the Canada Research Chairs program, which made possible the realization of this research and the publication of this paper. [PERSON] is the Canada Research Chair Tier 1 Holder in Aircraft Modeling and Simulation New Technologies.  
Not applicable.  
Special thanks are dues to the Natural Sciences and Engineering Research Council of Canada (NSERC) for the Canada Research Chair Tier 1 in Aircraft Modeling and Simulation Technologies funds. We would also like to thank [PERSON] and [PERSON] for their support at ETS, as well as Hydra Technologies' team members [PERSON], [PERSON], and [PERSON] in Mexico. Finally, we wish to express our appreciation to the Canada Foundation for Innovation CFI, the Ministere de l'Economie et de l'Innovation and Hydra Technologies for their support of the acquisition of the UAS-S4 at the LARASE.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.