Using of Wavelets for Secret Key Generation: A Measurement Based Study

AlpTopal, Ozan; Liang, Zekai; Ascheid, Gerd; Dartmann, Guido; Karabulut-Kurt, Günes

doi:10.1109/telfor.2018.8611930

Cited by 3 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(1) PCA [19]: using principal component analysis algorithm to reduce hardware fngerprint variance during channel measurements. (2) WAKG [20]: using wavelet analysis to preprocess the channel estimation to improve channel correlation. (3) SGF [21]: the Savitzky Golay Filter (SGF) method is used to increase the correlation of RSS values between users and access points.…”

Section: Crcnet Performance Evaluation Te Function Ofmentioning

confidence: 99%

“…Researchers adopt manually designed feature extraction algorithms to extract reciprocal features from weak reciprocal channels. One direction of research in feature extraction methods is to use traditional linear transforms such as the discrete cosine transform (DCT) [14], principal component analysis (PCA) [15,19], and wavelet transform [10,20]. Another research direction is to extract nonlinear features from the original channel response, such as amplitude, phase, and power delay distribution [13,21,22].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Chen

et al. 2023

Security and Communication Networks

View full text Add to dashboard Cite

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.

show abstract

Section: Crcnet Performance Evaluation Te Function Ofmentioning

confidence: 99%

Section: Introductionmentioning

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

View full text Add to dashboard Cite

show abstract

“…The authors of [12] make use of principle component analysis to decrease the key disagreement rates at the nodes. In [13], the authors utilize a wavelet-based pre-processing to eliminate the dissimilarities of the channel observations. As described in the Figure 1, we consider any three ISLs of three distinct spacecrafts.…”

Section: A Related Workmentioning

confidence: 99%

Securing the Inter-Spacecraft Links: Doppler Frequency Shift based Physical Layer Key Generation

Topal

Kurt

Yanıkömeroğlu

2020

2020 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE)

View full text Add to dashboard Cite

We propose a novel physical layer secret key generation method for the inter-spacecraft communication links. By exploiting the Doppler frequency shifts of the reciprocal spacecraft links as a unique secrecy source, spacecrafts aim to obtain identical secret keys from their individual observations. We obtain theoretical expressions for the key disagreement rate (KDR). Using generalized Gauss-Laguerre quadrature, we derive closed form expressions for the KDR. Through numerical studies, the tightness of the provided approximations are shown. Both the theoretical and numerical results demonstrate the validity and the practicality of the presented physical layer key generation procedure considering the security of the communication links of spacecrafts.

show abstract

Securing the Inter-Spacecraft Links: Physical Layer Key Generation From Doppler Frequency Shift

Topal

Kurt

Yanıkömeroğlu

2021

IEEE J. Radio Freq. Identif.

View full text Add to dashboard Cite

Using of Wavelets for Secret Key Generation: A Measurement Based Study

Cited by 3 publications

References 11 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

Securing the Inter-Spacecraft Links: Doppler Frequency Shift based Physical Layer Key Generation

Securing the Inter-Spacecraft Links: Physical Layer Key Generation From Doppler Frequency Shift

Contact Info

Product

Resources

About