DroneKey: A Drone-Aided Group-Key Generation Scheme for Large-Scale IoT Networks

Han, Dianqi; Li, Ang; Li, Jiawei; Zhang, Yan; Li, Tao

doi:10.1145/3460120.3484789

Cited by 9 publications

(2 citation statements)

References 25 publications

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“…Te main challenge in applying deep learning to physical layer key generation is the additional need for computational resources. Terefore, we choose to train the network on the base station side and deploy it to meet the demand of training and storing the network without additional consumption on the user side [28,35]. Moreover, the network's training can be conducted in the cloud without consuming node resources.…”

Section: Channel Reciprocal Compensation Networkmentioning

confidence: 99%

A Physical Layer Key Generation Scheme Based on Deep Learning Compensation and Balanced Vector Quantization

Chen

et al. 2023

Security and Communication Networks

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Channel reciprocity is the foundation for physical layer key generation, which is influenced by noise, hardware impairments, and synchronization offsets. Weak channel reciprocity will result in a high key disagreement rate (KDR). The existing solutions for improving channel reciprocity cannot achieve satisfactory performance improvements. Furthermore, the existing quantization algorithms generally use one-dimensional channel features to quantize and generate secret keys, which cannot fully utilize channel information. The multidimensional vector quantization technique also needs to improve in terms of randomness and time complexity. This paper proposes a physical layer key generation scheme based on deep learning and balanced vector quantization. Specifically, we build a channel reciprocity compensation network (CRCNet) to learn the mapping relationship between Alice and Bob’s channel measurements. Alice compensates for channel measurements via a trained CRCNet to reduce channel measurement errors between legitimate users and enhance channel reciprocity. We also propose a balanced vector quantization algorithm based on integer linear programming (ILP-BVQ). ILP-BVQ reduces the time complexity of quantization on the basis of ensuring key randomness and a low KDR. Simulation results showed that the proposed CRCNet performs better in terms of channel reciprocity and KDR, while the proposed ILP-BVQ algorithm improves time consumption and key randomness.

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Section: Channel Reciprocal Compensation Networkmentioning

confidence: 99%

A Physical Layer Key Generation Scheme Based on Deep Learning Compensation and Balanced Vector Quantization

Chen

et al. 2023

Security and Communication Networks

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“…the second is source authentication, which ensures that the messages originate from a specific source. Group authentication is generally addressed by group key management techniques [7][8][9][10]. However, source authentication is more difficult because the group key can't be used to verify a specific multicast source, as it is known by the group members.…”

Section: Introductionmentioning

confidence: 99%

Selective Hash-based Approach for Multicast Source Authentication in SDMN

Eltaief

Kamel

Youssef

2023

Preprint

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Multicast services offer a significant advantage of resource conservation for multicast senders and increase network efficiency by reducing network traffic. However, treating security concerns such as source authentication is important, especially for applications like online news, IPTV, video streaming, and stock quote distribution. Despite extensive research efforts to provide source authentication for multicast applications, there is no efficient approach that can effectively handle the requirements of the dynamic nature of multicast mobile receivers and networks. This paper provides an analysis of 22 years of research on source authentication mechanisms with non-repudiation. The majority of these mechanisms do not consider the dynamic nature of mobile users and networks. In response to this, we propose a new selective hash-based approach for multicast source authentication in Software Defined Mobile Network (SDMN). Using the advantages of the controller global view in SDMN, our approach is designed to operate in mobile and dynamic environments, provide non-repudiation, and tolerate packet loss. Our simulation results demonstrate that the proposed mechanism utilizes resources effectively, reduces communication and delay overhead, and works well in dynamic scenarios.

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Flex-CC: A flexible connected chains scheme for multicast source authentication in dynamic SDN environment

Eltaief

2022

Computer Networks

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DroneKey: A Drone-Aided Group-Key Generation Scheme for Large-Scale IoT Networks

Cited by 9 publications

References 25 publications

A Physical Layer Key Generation Scheme Based on Deep Learning Compensation and Balanced Vector Quantization

A Physical Layer Key Generation Scheme Based on Deep Learning Compensation and Balanced Vector Quantization

Selective Hash-based Approach for Multicast Source Authentication in SDMN

Flex-CC: A flexible connected chains scheme for multicast source authentication in dynamic SDN environment

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