Comparing Some Pseudo-Random Number Generators and Cryptography Algorithms Using a General Evaluation Pattern

Abstract

Since various pseudo-random algorith ms and sequences are used for cryptography of data or as init ial values for starting a secure communicat ion, how these algorith ms are analyzed and selected is very important. In fact, given the g rowingly extensive types of pseudorandom sequences and block and stream cipher algorith ms, selection of an appropriate algorith m needs an accurate and thorough investigation. Also, in order to generate a pseudo-random sequence and generalize it to a cryptographer algorith m, a co mprehensive and regular framework is needed, so that we are enabled to evaluate the presented algorith m as quick as possible. The purpose of this study is to use a number of pseudo-random number generators as well as popular cryptography algorith ms, analyze them in a standard framework and observe the results obtained in each stage. The investigations are like a match between d ifferent algorith ms, such that in each stage, weak algorith ms are eliminated using a standard method and successful algorith ms enter the next stage so that the best algorith ms are chosen in the final stage. The main purpose of this paper is to certify the approved algorithm.

Improved Salsa20 Stream Cipher Diffusion Based on Random Chaotic Maps

“…Ahmad.G.et.al. in [25] generated pseudorandom numbers and employed them with a variety of encryption methods, including Salsa20. The findings of NIST measures were used to assess different encryption algorithms that used pseudorandom numbers, and the results showed that utilizing random numbers with the Salsa20 stream cipher algorithm obtained faster implementation and more diffusion than the original Salsa20 algorithm.…”

“…A chaotic system or computational intelligence (CI) is therefore the ideal answer. Several techniques for chaotic systems have been devised [12]- [16]. The application of chaos theory, a kind of nonlinear system, in cryptography has recently been made to address issues with existing encryption techniques, which are losing their ability to provide quick and secure encryption for large amounts of data simultaneously [15].…”

“…Chaotic systems are extremely sensitive to changes in beginning conditions L. A. Muhalhal et al and parameter values, which allows for the formation of a wide range of chaotic sequences. The chaotic sequences that are generated [12], [13], [15], [17] are neither periodic nor convergent.…”

Performance Analysis of Cryptographic Pseudorandom Number Generators

“…Another research [9] discussed how one can solve the issue of finding a proper framework that will allow generation of a pseudorandom sequester to be generalized into the cryptographic algorithm. This condition is necessary to ensure that the algorithm can be evaluated as swiftly as possible.…”

Post-quantum cloud security and data exchange using artificial intelligence.

“…Block ciphers process a fixed-size plaintext block to output a ciphertext block of the same size while stream ciphers encrypts each plaintext bit or character independently, using a cipher and an encryption key. These output are considered to be randomized, given an encryption and/or decryption algorithm and the key [85] [62]. One of the oldest techniques of PRNGs is the Mid-Square method [48] [23].…”