A comprehensive survey on security, privacy issues and emerging defence technologies for UAVs

Hadi, Hassan Jalil; Cao, Yue; Nisa, Khaleeq Un; Jamil, Abdul Majid; Ni, Qiang

doi:10.1016/j.jnca.2023.103607

Cited by 62 publications

(24 citation statements)

References 96 publications

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“…To mitigate privacy risks associated with TCP variants, several strategies can be employed. Firstly, encryption technologies such as Transport Layer Security (TLS) should be implemented to protect the confidentiality and integrity of TCP communications, particularly for sensitive data transmissions over untrusted networks [81], [82]. Encrypting TCP payloads ensures that even if adversaries intercept the traffic, they cannot decipher the content without the appropriate cryptographic keys [83], [84].…”

Section: Privacy Issuesmentioning

confidence: 99%

A survey of transmission control protocol variants

Lydiah Moraa Machora

2024

World J. Adv. Res. Rev.

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TCP (Transmission Control Protocol), is a reliable connection oriented end-to-end protocol. It contains within itself, mechanisms for ensuring reliability by requiring the receiver to acknowledge the segments that it receives. The network is not perfect and a small percentage of packets are lost enroute, either due to network error or due to the fact that there is congestion in the network and the routers are dropping packets. TCP ensures reliability by starting a timer whenever it sends a segment. If it does not receive an acknowledgement from the receiver within the ‘time-out’ interval then it retransmits the segment. In this paper a review of various TCP is carried out. There are a number of TCP variants for application in the management of network efficiency in terms of network congestion and transmission efficiency. These variants include: - TCP Tahoe, TCP Reno, TCP New Reno, TCP Vegas, TCP SACK, TCP FACK, TCP Asym, TCP RBP, Full TCP and TCP CUBIC. Therefore, the main objective of this paper is to study the tcp types on the network performance variances. All have different features and advantages but with maximal throughput as main objective, which are termed as the clones of TCP, have been incorporated into TCP/IP protocol for handling congestion efficiently in different network scenarios.

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Section: Privacy Issuesmentioning

confidence: 99%

A survey of transmission control protocol variants

Lydiah Moraa Machora

2024

World J. Adv. Res. Rev.

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“…are used to initiate restricted UAV control functions on mobility constraints. Trusted cost magnitudes are computed as indicated in equations ( 10) and (11). The procedural descriptions are illustrated as L R P : and G R P : for internal and global trust magnitude costs, respectively.…”

Section: Let Assume 'mentioning

confidence: 99%

Reactive Handover Coordination System with Regenerative Blockchain Principles for Swarm Unmanned Aerial Vehicles

Rajasoundaran

Kumar

Selvi

et al. 2023

Preprint

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Aerial warfare systems in various countries highly expect Swarm Unmanned Aerial Vehicles (UAVs) and Flying Adhoc Networks (FANETs) for defense stability. However, swarm UAVs face many challenges, like security breaches, malfunctions, link failures, and handover failures. UAVs can easily malfunction by external threats to create data loss, theft, signal jamming, misrouting, false handover, and location spoofing. Failures in UAV handover principles make overall gradual downtime in FANET. Against these issues, existing UAV protection mechanisms deliver location monitoring mechanisms (graphs and trees), multi-link handover mechanisms, and distributed authentication principles. Anyhow, the implantation of recent-day techniques may fail against migrating attacker events executed in electronic warfare systems. The methods need improvements in protecting Multi-UAV layers through end-to-end security principles. On the research problem, the proposed Reactive Handover Coordination System with Regenerative Blockchain Principles (RHCRB) takes novel high-security features specially made for swarm UAVs. RHCRB implements more crucial distributed functions in each UAV on demand. The executed operations of RHCRB comprise trusted location monitoring schemes (internal and cooperative UAV movements), dynamic location-based cost magnitude calculations, regenerative blockchain principles (authentication of each UAV and active edges), confidential link management principles, secure handover coordination, and on-demand topology management principles. The technical aspects of RHCRB build lightweight and shielded handover principles against airfield vulnerabilities. The proposed model suggests implementing an entirely protected handover environment from node localization to handover events. The inspired technical aspects of RHCRB contribute to the swarm UAV environment through confidential (Advanced Encryption Standard (AES)) and distributed authentication (blockchain-based node and edge management) principles to engage protected handover practices. The experimental section of this article has the testbed in FlyNetSim tool for implementing RHCRB and notable recent security techniques such as the Internet of Vehicles with Decentralized Blockchains (IoV-DB), Group Handover for Internet of Defense (GH-IoD), and Handover and Optimized Security Principles for UAVs (HOOPOE). The results show RHCRB's 8–14% of betterment than existing techniques through various measures.

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“…A good flight attitude of UAVs is the key to their excellent mission accomplishment, but the complex working environment and unknown external interference make the flight attitude of UAVs to be affected, which puts the UAV in * Author to whom any correspondence should be addressed. a faulty state and seriously threatens the personal and property safety of the staffs [2,3]. Fault diagnosis technology can detect, analyze and evaluate the bad operation state of unmanned systems, and the study of fault diagnosis technology of UAV can improve the flight safety of UAV, which has important practical significance.…”

Section: Introductionmentioning

confidence: 99%

Unmanned aerial vehicle fault diagnosis based on ensemble deep learning model

Huang,

Liang,

Dai

et al. 2024

Meas. Sci. Technol.

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To address the problems of external interference during unmanned aerial vehicle (UAV) flight and the low accuracy and weak generalization ability of the current single fault diagnosis model, this work proposes a weighted ensemble deep learning UAV fault diagnosis method. First, considering the differences in training methods and fault feature recognition principles of deep networks with different structures, three hybrid fault diagnosis models are constructed. These models are constructed by combining convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM) and bidirectional gate recurrent unit (BiGRU). CNN is used to extract the features of the UAV flight data and the obtained feature information is fed into BiLSTM and BiGRU to explore the fault information inherent in the time series data. Then, the three hybrid fault diagnosis models are used as the individual model of the ensemble learning algorithm, and the weights of the three individual models are optimized using a random grid search algorithm to construct a UAV fault diagnosis model based on hybrid deep learning weighted ensemble, which further improves the fault diagnosis performance. Finally, it is demonstrated experimentally that the proposed hybrid deep learning weighted ensemble fault diagnosis model can effectively identify the fault of UAV with an accuracy of 99.22% and 99.62% on binary and multivariate classification, respectively, and reflects better generalization performance in the metrics of precision, recall, and F1 score.

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A comprehensive survey on security, privacy issues and emerging defence technologies for UAVs

Cited by 62 publications

References 96 publications

A survey of transmission control protocol variants

A survey of transmission control protocol variants

Reactive Handover Coordination System with Regenerative Blockchain Principles for Swarm Unmanned Aerial Vehicles

Unmanned aerial vehicle fault diagnosis based on ensemble deep learning model

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