Global Analysis of Active Defense Technologies for Unmanned Aerial Vehicle

Lyu, Chenyang; Zhan, Reggie

doi:10.1109/maes.2021.3115205

Cited by 38 publications

(16 citation statements)

References 110 publications

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“…IoT-based drones for agriculture were taken into consideration by Saha et al [33]. According to Lyu et al [34], commercial drones must deal with issues such as drone data theft, UAV theft, and drone hijacking. Jares et al [35] provided remedies and responses to security-related problems.…”

Section: A Drone Security Threatsmentioning

confidence: 99%

IoT Empowered Smart Cybersecurity Framework for Intrusion Detection in Internet of Drones

Ashraf

Manickam

Zia

et al. 2023

Preprint

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The emergence of drone-based innovative cyber security solutions integrated with the Internet of Things (IoT) has revolutionized navigational technologies with robust data communication services across multiple platforms. This advancement leverages machine learning and deep learning methods for future progress. In recent years, there has been a significant increase in the utilization of IoT-enabled drone data management technology. Industries ranging from industrial applications to agricultural advancements, as well as the implementation of smart cities for intelligent and efficient monitoring. However, these latest trends and drone-enabled IoT technology developments have also opened doors to malicious exploitation of existing IoT infrastructures. This raises concerns regarding the vulnerability of drone networks and security risks due to inherent design flaws and the lack of cybersecurity solutions and standards. The main objective of this study is to examine the latest privacy and security challenges impacting the network of drones (NoD). The research underscores the significance of establishing a secure and fortified drone network to mitigate interception and intrusion risks. The proposed system effectively detects cyber-attacks in drone networks by leveraging deep learning and machine learning techniques. Furthermore, the model's performance was evaluated using well-known drones’ UNSW-NB15, CICIDS2017, and KDDCup 99 datasets. We have tested the multiple hyperparameter parameters for optimal performance and classify data instances and maximum efficacy in the NoD framework. The model achieved exceptional efficiency and robustness in NoD. The system attains precision values of 89.10% and 90.16%, accuracy rates of 91.00% and 91.36%, recall values of 81.13% and 90.11%, and F-measure values of 88.11% and 90.19% for the respective evaluation metrics.

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Section: A Drone Security Threatsmentioning

confidence: 99%

IoT Empowered Smart Cybersecurity Framework for Intrusion Detection in Internet of Drones

Ashraf

Manickam

Zia

et al. 2023

Preprint

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“…In Saha et al [ 24 ], the use of IoT-based drones in agriculture was considered. As stated by Lyu et al [ 25 ], drone hijacking, drone data theft, and UAV theft are all problems that commercial drones confront. In Jares et al [ 26 ], countermeasures and solutions to security challenges were presented.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Cybersecurity Framework for IoT-Empowered Drones: Machine Learning Perspective

Aldaej

Ahanger

Atiquzzaman

et al. 2022

Sensors

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Drone advancements have ushered in new trends and possibilities in a variety of sectors, particularly for small-sized drones. Drones provide navigational interlocation services, which are made possible by the Internet of Things (IoT). Drone networks, on the other hand, are subject to privacy and security risks due to design flaws. To achieve the desired performance, it is necessary to create a protected network. The goal of the current study is to look at recent privacy and security concerns influencing the network of drones (NoD). The current research emphasizes the importance of a security-empowered drone network to prevent interception and intrusion. A hybrid ML technique of logistic regression and random forest is used for the purpose of classification of data instances for maximal efficacy. By incorporating sophisticated artificial-intelligence-inspired techniques into the framework of a NoD, the proposed technique mitigates cybersecurity vulnerabilities while making the NoD protected and secure. For validation purposes, the suggested technique is tested against a challenging dataset, registering enhanced performance results in terms of temporal efficacy (34.56 s), statistical measures (precision (97.68%), accuracy (98.58%), recall (98.59%), F-measure (99.01%), reliability (94.69%), and stability (0.73).

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“…Various techniques for monitoring and preventing UAV attacks were studied and described along with case studies in [8]. In [9], the authors conducted extensive market research of 581 counter-unmanned aerial system (C-UAS) systems from 282 manufacturers in 39 countries around the world based on the literature and practice, making it a four-way statistical analysis. Based on this, they classified and summarized the most advanced weapon systems according to different technologies.…”

Section: Introductionmentioning

confidence: 99%

A Software Defined Radio Based Anti-UAV Mobile System with Jamming and Spoofing Capabilities

Ferreira

Gaspar

Sebastião

et al. 2022

Sensors

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The number of incidents between unmanned aerial vehicles (UAVs) and aircrafts at airports and airfields has been increasing over the last years. To address the problem, in this paper we describe a portable system capable of protecting areas against unauthorized UAVs, which is based on the use of low-cost SDR (software defined radio) platforms. The proposed anti-UAV system supports target localization and integrates effective jamming techniques with the generation of global positioning system (GPS) spoofing signals aimed at the drone. Real-life tests of the implemented prototype have shown that the proposed approach is capable of stopping the reliable reception of radionavigation signals and can also divert or even take control of unauthorized UAVs, whose flight path depends on the information obtained by the GPS system.

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Global Analysis of Active Defense Technologies for Unmanned Aerial Vehicle

Cited by 38 publications

References 110 publications

IoT Empowered Smart Cybersecurity Framework for Intrusion Detection in Internet of Drones

IoT Empowered Smart Cybersecurity Framework for Intrusion Detection in Internet of Drones

Smart Cybersecurity Framework for IoT-Empowered Drones: Machine Learning Perspective

A Software Defined Radio Based Anti-UAV Mobile System with Jamming and Spoofing Capabilities

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