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<p>In recent times, there have been several creations of models, methodologies, and ways to make the best, optimized and secure way of establishing connections between unmanned aerial vehicles (UAV’s). The utilization of federated learning has led to a substantial augmentation in the domains of both deep and machine learning, with the objective of optimizing artificial intelligence techniques and tools. This enhancement is particularly pertinentin the context of 5G-enabled cybersecurity communications within automotive systems, with a specific focus on the Internet of Drones (IoD). Recent studies have shown that multiple security breaches can be dealt using a combination of layers that can work into the drone, the base or even both at the same time while having a wide range of networking. Some models even succeeded in being able to fully operate without the need for a human monitoring system using methodologies like Machine Learning (ML) to be autonomous. This paper presents a comprehensive analysis and technical discussion on the federated, machine and deep learning approaches when it comes to the world of 5G-enabled cybersecurity communications and intrusion detection systems in the IoD. This paper aims to conceive and highlight various self-adaptive learning approaches and their effect on cybersecurity communications models and algorithms that help the use of drones in general-cases or in mission-related cases.</p>

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Federated Machine Learning Approaches for Cybersecurity Communications in Internet of Drones

  • 期刊论文

  • 发表日期：

    2026年1月8日

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  • Sami Abou El Faouz,
  • Alireza Souri,
  • Nihat İnanç

摘要

In recent times, there have been several creations of models, methodologies, and ways to make the best, optimized and secure way of establishing connections between unmanned aerial vehicles (UAV’s). The utilization of federated learning has led to a substantial augmentation in the domains of both deep and machine learning, with the objective of optimizing artificial intelligence techniques and tools. This enhancement is particularly pertinentin the context of 5G-enabled cybersecurity communications within automotive systems, with a specific focus on the Internet of Drones (IoD). Recent studies have shown that multiple security breaches can be dealt using a combination of layers that can work into the drone, the base or even both at the same time while having a wide range of networking. Some models even succeeded in being able to fully operate without the need for a human monitoring system using methodologies like Machine Learning (ML) to be autonomous. This paper presents a comprehensive analysis and technical discussion on the federated, machine and deep learning approaches when it comes to the world of 5G-enabled cybersecurity communications and intrusion detection systems in the IoD. This paper aims to conceive and highlight various self-adaptive learning approaches and their effect on cybersecurity communications models and algorithms that help the use of drones in general-cases or in mission-related cases.