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Aircraft trajectory prediction enhanced through resilient generative adversarial networks secured by blockchain: Application to UAS-S4 Ehécatl

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Hashemi, Seyed Mohammad, Hashemi, Seyed Ali, Botez, Ruxandra Mihaela and Ghazi, Georges. 2023. « Aircraft trajectory prediction enhanced through resilient generative adversarial networks secured by blockchain: Application to UAS-S4 Ehécatl ». Applied Sciences, vol. 13, nº 17.
Compte des citations dans Scopus : 4.

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Abstract

This paper introduces a novel and robust data-driven algorithm designed for Aircraft Trajectory Prediction (ATP). The approach employs a Neural Network architecture to predict future aircraft trajectories, utilizing input variables such as latitude, longitude, altitude, heading, speed, and time. The model’s foundation is rooted in the Generative Adversarial Network (GAN) framework, known for its inherent generative capabilities, rendering it remarkably resilient against Adversarial Attacks. To enhance its credibility, the Blockchain is employed as a Ledger Technology (LT) to securely store legitimate predicted values utilized in subsequent trajectory predictions. The Blockchain ensures that only authorized and non-adversarial samples are stored in the blocks, rejecting any adversarial predictions. In the validation process, trajectory data for training the GAN model were generated through the UAS-S4 Ehécatl simulation model. The performance evaluation relies on the model’s resistance to adversarial attacks, measured by fooling rates. The results acquired affirm the excellent efficacy of the GAN model, Secured by Blockchain, approaching against adversarial attacks.

Item Type: Peer reviewed article published in a journal
Professor:
Professor
Botez, Ruxandra
Ghazi, Georges
Affiliation: Génie des systèmes, Génie des systèmes
Date Deposited: 26 Sep 2023 20:00
Last Modified: 17 Oct 2023 18:35
URI: https://espace2.etsmtl.ca/id/eprint/27853

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