Lightweight Internet of Things Encryption Using Galois Extension Field Arithmetic

McGinthy, Jason M.; Michaels, Alan J.

doi:10.1109/cybermatics_2018.2018.00046

Cited by 5 publications

(7 citation statements)

References 25 publications

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“…Lastly, a comparison of memory and computational requirements between the cryptosystem modes and AES is made to illustrate the point that the proposed block cipher in this paper is a better option for IoT devices. All comparisons include metrics on the GEF stream cipher variation [23] as an additional baseline. Confusion and other cryptographic properties generally offered in block ciphers are not included in this analysis because the proposed cryptographic algorithm does not include techniques to introduce them.…”

Section: Resultsmentioning

confidence: 99%

“…Galois Extension Fields were used as a stream cipher combination technique in the previous paper as an alternative to traditional methods like XOR. Additionally, they were proven to be an effective technique for fast, power-efficient stream cipher encryption in IoT devices [23]. This paper extends this technique to be usable as a block cipher by constructing an upper-right triangular matrix with outputs from a PRNG in GF(2 k ).…”

Section: Galois Extension Fieldsmentioning

confidence: 97%

“…As before, each value for the key could be an independent cryptographic algorithm that outputs values in GF(2 k ). Unlike [23], the encryption/decryption processes are not independent and require coordinated party effort.…”

Section:  mentioning

confidence: 99%

“…The results are also displayed in Table 1. The stream cipher variation of the GEF cryptosystem from [23] was also implemented in MATLAB and used to encrypt the same plaintext as ECB mode and CBC mode, resulting in a Shannon entropy of 7.9998 bits. The distribution, shown in Figure 7, indicates a mean of 3906.25 and standard deviation of 63.56.…”

Section: Entropy Analysismentioning

confidence: 99%

“…A basic numeric example is provided to make the system more accessible for readers. Section 4 discusses results based on a series of measurements used to judge the strength of the cryptosystem, and compares these results to the stream cipher variation [23] and AES. The results of the NIST statistical test suite are compiled and displayed for each mode of this crytographic algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Designing a Block Cipher in Galois Extension Fields for IoT Security

George

Michaels

2021

IoT

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This paper focuses on a block cipher adaptation of the Galois Extension Fields (GEF) combination technique for PRNGs and targets application in the Internet of Things (IoT) space, an area where the combination technique was concluded as a quality stream cipher. Electronic Codebook (ECB) and Cipher Feedback (CFB) variations of the cryptographic algorithm are discussed. Both modes offer computationally efficient, scalable cryptographic algorithms for use over a simple combination technique like XOR. The cryptographic algorithm relies on the use of quality PRNGs, but adds an additional layer of security while preserving maximal entropy and near-uniform distributions. The use of matrices with entries drawn from a Galois field extends this technique to block size chunks of plaintext, increasing diffusion, while only requiring linear operations that are quick to perform. The process of calculating the inverse differs only in using the modular inverse of the determinant, but this can be expedited by a look-up table. We validate this GEF block cipher with the NIST test suite. Additional statistical tests indicate the condensed plaintext results in a near-uniform distributed ciphertext across the entire field. The block cipher implemented on an MSP430 offers a faster, more power-efficient alternative to the Advanced Encryption Standard (AES) system. This cryptosystem is a secure, scalable option for IoT devices that must be mindful of time and power consumption.

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Section: Resultsmentioning

confidence: 99%

Section: Galois Extension Fieldsmentioning

confidence: 97%

Section:  mentioning

confidence: 99%

Section: Entropy Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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