Attack and Defense on Aircraft Trajectory Prediction Algorithms

Iersel, Quincy G. van; Mendoza, Alejandro Murrieta; Patron, Roberto S. Felix; Hashemi, Seyed Mohammad; Botez, Ruxandra

doi:10.2514/6.2022-4027

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…While NN ATP models demonstrated strong performance in terms of prediction accuracy, they exhibited vulnerability to adversarial attacks [32]. Adversarial attacks on trajectory prediction models pose serious risks to various applications, including flight safety and operational efficiency.…”

Section: Introductionmentioning

confidence: 99%

Aircraft Trajectory Prediction Enhanced through Resilient Generative Adversarial Networks Secured by Blockchain: Application to UAS-S4 Ehécatl

Hashemi,

Botez

et al. 2023

Applied Sciences

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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.

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Section: Introductionmentioning

confidence: 99%

Aircraft Trajectory Prediction Enhanced through Resilient Generative Adversarial Networks Secured by Blockchain: Application to UAS-S4 Ehécatl

Hashemi,

Botez

et al. 2023

Applied Sciences

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“…The LCP, rooted in L1 approximation, balanced asymmetry, absolute infinite flow, and unbounded interaction graphs, has been the most renowned approach for managing SDAs [31]. Nonetheless, the introduction of blockchain technology has exposed vulnerabilities in the LCP, particularly when confronted with soft attacks and hardware failures [32,33].…”

Section: Introductionmentioning

confidence: 99%

Blockchain PoS and PoW Consensus Algorithms for Airspace Management Application to the UAS-S4 Ehécatl

Hashemi,

Botez,

Ghazi

2023

Algorithms

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This paper introduces an innovative consensus algorithm for managing Unmanned Aircraft System Traffic (UTM) through blockchain technology, a highly secure consensus protocol, to allocate airspace. A smart contract was developed on the Ethereum blockchain for allocating airspace. This technique enables the division of the swarm flight zone into smaller sectors to decrease the computational complexity of the algorithm. A decentralized voting system was established within these segmented flight zones, utilizing two primary methodologies: Proof of Work (PoW) and Proof of Stake (PoS). By employing 1000 UAS-S4s across various locations and heading angles, a swarm flight zone was generated. The efficiency of the devised decentralized consensus system was assessed based on error rate and validation time. Despite PoS displaying greater efficiency in cumulative probability for block execution, the comparative analysis indicated PoW outperformed PoS concerning the potential for conflicts among UASs.

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A Novel Fault-Tolerant Air Traffic Management Methodology Using Autoencoder and P2P Blockchain Consensus Protocol

Hashemi

Botez

et al. 2023

Aerospace

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This paper presents a methodology for designing a highly reliable Air Traffic Management and Control (ATMC) methodology using Neural Networks and Peer-to-Peer (P2P) blockchain. A novel data-driven algorithm was designed for Aircraft Trajectory Prediction (ATP) based on an Autoencoder architecture. The Autoencoder was considered in this study due to its excellent fault-tolerant ability when the input data provided by the GPS is deficient. After conflict detection, P2P blockchain was used for securely decentralized decision-making. A meta-controller composed of this Autoencoder, and P2P blockchain performed the ATMC task very well. A comprehensive database of trajectories constructed using our UAS-S4 Ehécatl was used for algorithms validation. The accuracy of the ATP was evaluated for a variety of data failures, and the high-performance index confirmed the excellent efficiency of the autoencoder. Aircraft were considered in several local encounter scenarios, and their trajectories were securely managed and controlled using our in-house Smart Contract software developed on the Ethereum platform. The Sharding approach improved the P2P blockchain performance in terms of computational complexity and processing time in real-time operations. Therefore, the probability of conflicts among aircraft in a swarm environment was significantly reduced using our new methodology and algorithm.

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Attack and Defense on Aircraft Trajectory Prediction Algorithms

Cited by 5 publications

References 16 publications

Aircraft Trajectory Prediction Enhanced through Resilient Generative Adversarial Networks Secured by Blockchain: Application to UAS-S4 Ehécatl

Aircraft Trajectory Prediction Enhanced through Resilient Generative Adversarial Networks Secured by Blockchain: Application to UAS-S4 Ehécatl

Blockchain PoS and PoW Consensus Algorithms for Airspace Management Application to the UAS-S4 Ehécatl

A Novel Fault-Tolerant Air Traffic Management Methodology Using Autoencoder and P2P Blockchain Consensus Protocol

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