A Functional Encryption Technique in UAV Integrated HetNet: A Proposed Model

Sharma, Diwankshi; Rashid, Aabid; Gupta, Sumeet; Gupta, Sachin Kumar

doi:10.5013/ijssst.a.20.s1.07

Cited by 9 publications

(9 citation statements)

References 9 publications

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“…Sharma et al 92 have proposed a functional encryption technique in UAVs that assisted HetNets to provide data security against malicious intrusion in dense urban scenarios. The implementation of FE is done in two cases: The first is between UE and MBS and the Second is between MBS and UE through UAVs.…”

Section: Background Studies and Reported Work In The State Of The Artmentioning

confidence: 99%

Flying through the secure fog: A complete study on UAV‐Fog in heterogeneous networks

Gupta

2022

Int J Communication

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Summary A drone or unmanned aerial vehicles (UAVs) is becoming a trending area for researchers worldwide. UAV's contribution is increasing in day‐to‐day life, whether it is in a military zone, disaster management, healthcare sector, smart cities, Internet of Things (IoT), urban air mobility, and many more. In contrast, UAV's limited computational capability and low‐energy sources pose significant challenges for real‐time data processing, storage, networking, and security that are critical in emergencies such as floods, earthquakes, and cyclones. UAVs are rapidly used to satisfy user requirements as well as services. As the demand for UAVs aided heterogeneous wireless networks increases in critical emergencies, fog computing serves several benefits to fulfill users' demands in terms of low latency, support, data storage, mobility, availability, scalability, and so on. This study aims to present a comprehensive study with their technical aspects for understanding fog computing, security issues, privacy concerns, and risks, along with its solutions. This paper suggests the collaboration of UAV‐Fog architecture based on the four‐tier network consisting of smart things, local UAVs, UAV‐Fog, and cloud server, to control UAV's data and also described some of the security issues faced by this cloud infrastructure. Further, this research article also sheds new light on some scenarios of UAV‐Fog for such deployments, applications, opportunities, challenges, and their major security threats and their countermeasures. Afterward, we design taxonomy of the collaboration of UAV‐Fog with their respective approaches.

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Section: Background Studies and Reported Work In The State Of The Artmentioning

confidence: 99%

Flying through the secure fog: A complete study on UAV‐Fog in heterogeneous networks

Gupta

2022

Int J Communication

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“…However, transferring high-resolution pictures from one resource site to another is challenging and risky. 21,22 To accomplish secure and low transmission bandwidth requirements, Feki et al 23 developed a federated deep learning system for the categorization of COVID-19 related x-rays. Deep learning methods like VGG16 and ResNet were used to attain testing accuracy of 90% to 95%.…”

Section: Related Workmentioning

confidence: 99%

“…In the current situation, AI categorization of COVID‐19‐related medical health data, such as x‐ray pictures, is in great demand for effective screening or diagnosis. However, transferring high‐resolution pictures from one resource site to another is challenging and risky 21,22 . To accomplish secure and low transmission bandwidth requirements, Feki et al 23 developed a federated deep learning system for the categorization of COVID‐19 related x‐rays.…”

Section: Introductionmentioning

confidence: 99%

Privacy protected internet of unmanned aerial vehicles for disastrous site identification

Khullar

Singh

2022

Concurrency and Computation

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Disastrous site identification through the internet of UAVs is a current research area that aims to improve data sharing by connecting servers to the internet. Internet-connected unmanned aerial vehicles (UAVs) for aerial image classification necessitated the sharing of large datasets between the number of connected flying machines. The artificial intelligence model training to predict images need to compromise data privacy, consume a lot of energy, and high requirement of data communication. This article aims to propose and implement federated deep learning trained aerial image classification for disastrous sites through the internet of UAVs. The proposed model provides privacy-preserving and resource efficient deep learning by sharing only models rather than large datasets of images. Multisystem client-server federated learning UAV architecture was implemented and comparatively analyzed on basis of parameters namely, training-testing accuracy, train-testing loss, RAM-CPU utilization.

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“…But the major limitation of this article is that no security implementation has been done for the important data of agencies. The authors in Reference 32 have only studied the theoretical claims about implementing the FE technique without verifying it. The authors of Reference 33 have proposed an intrusion detection system for anomaly estimation in networks based on UAVs.…”

Section: Related Workmentioning

confidence: 99%

A novel approach for securing data against intrusion attacks in unmanned aerial vehicles integrated heterogeneous network using functional encryption technique

Sharma¹,

Gupta

Rashid

et al. 2020

Trans Emerging Tel Tech

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As the number of user equipment (UE) in any heterogeneous network (HetNet) assisted by unmanned aerial vehicles (UAV) continues to grow, so does the number of intruder nodes. The intruder/malicious nodes are able to interfere with the ongoing data transmission in the network and carry out different kinds of active and passive attacks such as spoofing, masquerading, impersonating, and so on in the network thus requiring an optimized security technique for the network. This article implements the novel functional encryption (FE) technique in the proposed UAV assisted HetNet model for the dense urban area to secure data against such intrusions. In this network model, UAV acts as a relay node for those UE which are in nonline‐of‐sight communication with macro based station (MBS). For securing the data transmission among UAV, UE, and MBS, FE technique is implemented in the network in two phases: the first phase between UE and MBS and the second phase between MBS and UE through UAV. During implementation, the Dolev‐Yao attack model is considered in which intruders are able to intercept or modify the UE data. The main objective of the FE technique implementation is to provide security from such intrusion attacks. The proposed methodology is validated using automated validation of Internet security protocols and applications (AVISPA) tool. The results of the AVISPA tool clearly indicate that the proposed technique is safe to implement in the UAV assisted HetNet, even in the presence of intruder nodes.

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A Functional Encryption Technique in UAV Integrated HetNet: A Proposed Model

Cited by 9 publications

References 9 publications

Flying through the secure fog: A complete study on UAV‐Fog in heterogeneous networks

Flying through the secure fog: A complete study on UAV‐Fog in heterogeneous networks

Privacy protected internet of unmanned aerial vehicles for disastrous site identification

A novel approach for securing data against intrusion attacks in unmanned aerial vehicles integrated heterogeneous network using functional encryption technique

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