Supervised Deep Learning Models for Detecting GPS Spoofing Attacks on Unmanned Aerial Vehicles

Khoei, Tala Talaei; Aissou, Ghilas; Shamaileh, Khair Al; Devabhaktuni, V.K.; Kaabouch, Naima

doi:10.1109/eit57321.2023.10187274

Cited by 3 publications

References 22 publications

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Deep learning: systematic review, models, challenges, and research directions

Talaei Khoei,

Ould Slimane,

Kaabouch

2023

Neural Comput & Applic

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The current development in deep learning is witnessing an exponential transition into automation applications. This automation transition can provide a promising framework for higher performance and lower complexity. This ongoing transition undergoes several rapid changes, resulting in the processing of the data by several studies, while it may lead to time-consuming and costly models. Thus, to address these challenges, several studies have been conducted to investigate deep learning techniques; however, they mostly focused on specific learning approaches, such as supervised deep learning. In addition, these studies did not comprehensively investigate other deep learning techniques, such as deep unsupervised and deep reinforcement learning techniques. Moreover, the majority of these studies neglect to discuss some main methodologies in deep learning, such as transfer learning, federated learning, and online learning. Therefore, motivated by the limitations of the existing studies, this study summarizes the deep learning techniques into supervised, unsupervised, reinforcement, and hybrid learning-based models. In addition to address each category, a brief description of these categories and their models is provided. Some of the critical topics in deep learning, namely, transfer, federated, and online learning models, are explored and discussed in detail. Finally, challenges and future directions are outlined to provide wider outlooks for future researchers.

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Deep learning: systematic review, models, challenges, and research directions

Talaei Khoei,

Ould Slimane,

Kaabouch

2023

Neural Comput & Applic

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Detecting Injection Attacks in ADS-B Devices Using RNN-Based Models

Khoei,

Ould Slimane,

Shamaileh

et al. 2024

2024 Integrated Communications, Navigation and Surveillance Conference (ICNS)

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Detecção de Ataques de GPS em Veículos Aéreos Não Tripulados com Classificação Multiclasse

Lemos,

Silva

2024

Anais Do XXIV Simpósio Brasileiro De Segurança Da Informação E De Sistemas Computacionais (SBSeg 2024)

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Veículos aéreos não tripulados (VANTs) têm sido cada vez mais utilizados em diversos domínios. Esses veículos geralmente dependem do Sistema de Posicionamento Global (GPS), o que os torna vulneráveis a ataques baseados em sinais de GPS falsos. Assim, este artigo propõe um Sistema de Detecção de Intrusão (IDS) que utiliza técnicas de aprendizado de máquina para detectar e identificar GPS Jamming e três tipos de ataques de GPS Spoofing. O classificador multiclasse proposto permite a identificação do tipo de ataque – algo essencial para determinar as medidas de proteção mais eficazes. A acurácia alcançada foi de 98,08%, com 2,6% de falsos negativos, diminuindo a probabilidade de ignorar ataques, algo essencial em infraestruturas com VANTs reais.

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Supervised Deep Learning Models for Detecting GPS Spoofing Attacks on Unmanned Aerial Vehicles

Cited by 3 publications

References 22 publications

Deep learning: systematic review, models, challenges, and research directions

Deep learning: systematic review, models, challenges, and research directions

Detecting Injection Attacks in ADS-B Devices Using RNN-Based Models

Detecção de Ataques de GPS em Veículos Aéreos Não Tripulados com Classificação Multiclasse

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