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Ad Hoc Networks

Physical layer secret key generation using discrete wavelet packet transform

Megha Santhosh Kumar, R. Ramanathan, M. Jayakumar, Devendra Kumar Yadav

Published Apr 30, 2021• 14 citations
Publisher
DOI
10.1016/j.adhoc.2021.102523

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Secured data transfer over the internet of things enables communication devices is a very challenging task. Due to the limitation of computational resources, classical cryptography algorithms are vulnerable to cyber threats and loss of integrity and information. Recently, physical layer secret key generation has played a vital role in secured communication on the internet of things. The physical layer key generation approach provides an alternate means of information security using the signal reciprocity of wireless communication. This paper proposed proficient dynamic secret key generation using derivates of transform functions. The derivates of transform functions such as DCT, DWT, DWPT, and many more light transform functions the physical layer key generation employs RSSI channel parameters in conjunction with the discrete wavelet transform to generate low computational secret keys for the authentication of IoT-enabled communication devices. The proposed algorithm is very efficient and reduces the bit disagreement of the key. We use MATLAB software defined radios to conduct the experiment, and we then evaluate the effectiveness of our post-processing techniques. Our approach is helpful for safeguarding future IoT devices since it enhances the ability of targeted nodes to establish matching secret keys while reducing the risk of an eavesdropper. The experimental results of key generation methods suggest that the proposed algorithm is very efficient instead of DCT and DWT.

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For the physical layer (PHY) key generation, two distant nodes exploit the reciprocal channel coefficients as a shared secret. These nodes may obtain erroneous channel estimates and generate highly correlated but not identical secret keys. In this work, we analyze the key error rate (KER) performances when different wavelet families are utilized as a pre-processing step in PHY key generation. Measurement-based KER results show that selection of the wavelet can significantly change the performance.

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Secured data transfer over the internet of things enables communication devices is a very challenging task. Due to the limitation of computational resources, classical cryptography algorithms are vulnerable to cyber threats and loss of integrity and information. Recently, physical layer secret key generation has played a vital role in secured communication on the internet of things. The physical layer key generation approach provides an alternate means of information security using the signal reciprocity of wireless communication. This paper proposed proficient dynamic secret key generation using derivates of transform functions. The derivates of transform functions such as DCT, DWT, DWPT, and many more light transform functions the physical layer key generation employs RSSI channel parameters in conjunction with the discrete wavelet transform to generate low computational secret keys for the authentication of IoT-enabled communication devices. The proposed algorithm is very efficient and reduces the bit disagreement of the key. We use MATLAB software defined radios to conduct the experiment, and we then evaluate the effectiveness of our post-processing techniques. Our approach is helpful for safeguarding future IoT devices since it enhances the ability of targeted nodes to establish matching secret keys while reducing the risk of an eavesdropper. The experimental results of key generation methods suggest that the proposed algorithm is very efficient instead of DCT and DWT.
For the physical layer (PHY) key generation, two distant nodes exploit the reciprocal channel coefficients as a shared secret. These nodes may obtain erroneous channel estimates and generate highly correlated but not identical secret keys. In this work, we analyze the key error rate (KER) performances when different wavelet families are utilized as a pre-processing step in PHY key generation. Measurement-based KER results show that selection of the wavelet can significantly change the performance.