Node to Node Secure Data Communication for IoT Devices Using Diffie-Hellman, AES, and MD5 Cryptographic Schemes

Kester, Quist-Aphetsi; Xenya, Michael Christopher

doi:10.1109/icsiot47925.2019.00022

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…Discrete chaotic maps, such as sine and logistic maps [17–23], can be used to generate 1D random and non‐ordered numbers in a non‐periodic chaotic sequence on the specific interval of control parameters. In this study, we combined the sine and logistic chaotic maps, the so‐called sine‐power chaotic map (SPCM), to establish an evident chaotic behaviour to produce secret keys, as

c_{h + 1} = \sin^{2} (\sqrt{| c_{h} |}) + 2 (1 - r) | c_{h} | (1 - 2 | c_{h} |), h = 0, 1, 2, …, N_{c}

where r is the control parameter, c 0 is the initial condition, as 0 < c 0 < 1, and N c is the sequence length.…”

Section: Methodsmentioning

confidence: 99%

“…Discrete chaotic maps, such as sine and logistic maps [17][18][19][20][21][22][23], can be used to generate 1D random and non-ordered numbers in a non-periodic chaotic sequence on the specific interval of control parameters. In this study, we combined the sine and logistic chaotic maps, the so-called sine-power chaotic map (SPCM), to establish an evident chaotic behaviour to produce secret keys, as…”

Section: Chaotic Map-based Key Generatormentioning

confidence: 99%

“…Hence, they can combine a multi-round cryptography protocol or an intelligent cryptography scheme to raise the complexity and security levels [19]. In asymmetric cryptography, such as Rivest-Shamir-Adleman and Diffie-Hellman algorithms [20][21][22], the public key is available to encrypt messages for sending them, and then the private key is owned by an authorized receiver for decrypting ciphers. This method has no information need for exchanging keys, thus overcoming the key distribution problem against statistical or analytic attacks.…”

mentioning

confidence: 99%

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Intelligent physiological signal infosecurity: Case study in photoplethysmography (PPG) signal

Lin

Pai

et al. 2021

IET Signal Processing

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A wrist‐based photoplethysmography (PPG) tool offers a simple and non‐invasive approach for applications in vital sign monitoring and healthcare. However, in a telecare network, these physiological signals ensure the authorisation demands for the domain of medical applications. Hence, this study proposes an intelligent method for PPG signal encryption and decryption. This method combines chaotic map and radial basis function network (RBFN) into symmetric cryptography with an adaptive scheme. The sine‐power chaotic map is used as a key generator of 256 non‐ordered numbers (key space) to set the private cipher codes. Two RBFNs are used to train an encryptor and a decryptor with the authorised cipher codes. Its substitution‐based infosecurity scheme can change the numerical values of PPG raw data for each encrypted communication, and the decrypted PPG data are further applied for time‐ and frequency‐domain analyses in clinical applications, such as heart and respiration rate analysis and arterial stiffness and upper extremity vascular disease evaluations. Through experimental results, the security levels are validated using the number of pixel change rate (NPCR), unified averaged changed intensity (UACI), and correlation analysis. The average NPCR, UACI, and correlation coefficient (CC) are 96.57%, 35.43%, and 0.005, respectively, between the plain PPG and the encrypted PPG data against hacker attacks. The larger‐the‐better of NPCR and UACI indexes and the smaller‐the‐better of CC index are obtained to evaluate the efficiency of the proposed cryptography method. The encrypted PPG also guarantees physiological signals of good quality in clinical applications. In addition, the performance of RBFN‐based method is superior in adaptive learning capability than that of the traditional learning method in real‐time applications. The cover image is based on the Original Research Paper Intelligent physiological signal infosecurity: Case study in photoplethysmography (PPG) signal by Chia Hung‐Lin et al., https://doi.org/10.1049/sil2.12089.

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