Pseudo Random Number Generator Algorithm Research Articles

Enhancing Smart Communication Security: A Novel Cost Function for Efficient S-Box Generation in Symmetric Key Cryptography

In the realm of smart communication systems, where the ubiquity of 5G/6G networks and IoT applications demands robust data confidentiality, the cryptographic integrity of block and stream cipher mechanisms plays a pivotal role. This paper focuses on the enhancement of cryptographic strength in these systems through an innovative approach to generating substitution boxes (S-boxes), which are integral in achieving confusion and diffusion properties in substitution–permutation networks. These properties are critical in thwarting statistical, differential, linear, and other forms of cryptanalysis, and are equally vital in pseudorandom number generation and cryptographic hashing algorithms. The paper addresses the challenge of rapidly producing random S-boxes with desired cryptographic attributes, a task notably arduous given the complexity of existing generation algorithms. We delve into the hill climbing algorithm, exploring various cost functions and their impact on computational complexity for generating S-boxes with a target nonlinearity of 104. Our contribution lies in proposing a new cost function that markedly reduces the generation complexity, bringing down the iteration count to under 50,000 for achieving the desired S-box. This advancement is particularly significant in the context of smart communication environments, where the balance between security and performance is paramount.

Modelling trivariate distribution of directional ocean data in the Barents Sea seasonal ice zone

The Arctic Ocean has proved to possess a wealth of natural oil and gas resources. Although the ice cover complicates marine industrial activities and transportation, exploratory drilling and processing facilities operation is possible in the region where the sea ice is only present for a portion of the year. This study focuses on the statistical characteristics of directional wave climate in the seasonal ice zone of the Barents Sea. The joint distributions of significant wave height, mean wave period, and mean wave direction were constructed using a mixture trivariate distribution model. The total ocean data were classified into four groups based on the relative weights of the energy content of wind wave and swell fields. The joint distribution for each wave component was constructed by the vine copula model, where the marginal distributions can be well described by the mixture univariate distribution models, the bivariate distributions of angular-linear variables were fitted by the Johnson-Wehrly model adequately, and the bivariate distribution of the linear-linear variable can be accurately modelled by a product of two Gaussian copulas. The results suggested that the mixture trivariate distribution for the total sea state constructed by a linear combination of the four submodels is capable of providing reasonable description for the directional wave data. To generate trivariate samples for model evaluation, an improved pseudorandom number generation algorithm was applied. Furthermore, the environmental contours associated with return periods of 1-, 5-, 20-, and 100-year in different directions were calculated and used to determine the extreme tensions of a semi-submersible platform model based on the global maxima approach.

Attacks and Defenses for Single-Stage Residue Number System PRNGs

This paper explores the security of a single-stage residue number system (RNS) pseudorandom number generator (PRNG), which has previously been shown to provide extremely high-quality outputs when evaluated through available RNG statistical test suites or in using Shannon and single-stage Kolmogorov entropy metrics. In contrast, rather than blindly performing statistical analyses on the outputs of the single-stage RNS PRNG, this paper provides both white box and black box analyses that facilitate reverse engineering of the underlying RNS number generation algorithm to obtain the residues, or equivalently key, of the RNS algorithm. We develop and demonstrate a conditional entropy analysis that permits extraction of the key given a priori knowledge of state transitions as well as reverse engineering of the RNS PRNG algorithm and parameters (but not the key) in problems where the multiplicative RNS characteristic is too large to obtain a priori state transitions. We then discuss multiple defenses and perturbations for the RNS system that fool the original attack algorithm, including deliberate noise injection and code hopping. We present a modification to the algorithm that accounts for deliberate noise, but rapidly increases the search space and complexity. Lastly, we discuss memory requirements and time required for the attacker and defender to maintain these defenses.

Pseudo-random number generator based on a generalized conservative Sprott-A system

Pseudo-random number generator (PRNG) has been widely used in digital image encryption and secure communication. This paper reports a novel PRNG based on a generalized Sprott-A system that is conservative. To validate whether the system can produce high quality chaotic signals, we numerically investigate its conservative chaotic dynamics and the complexity based on the approximate entropy algorithm. In this PRNG, we first select an initial value as a key to generate conservative chaotic sequence, then a scrambling operation is introduced into the process to enhance the complexity of the sequence, which is quantified by the binary quantization method. The national institute of standards and technology statistical test suite is used to test the randomness of the scrambled sequence, and we also analyze its correlation, keyspace, key sensitivity, linear complexity, information entropy and histogram. The numerical results show that the binary random sequence produced by the PRNG algorithm has the advantages of the large keyspace, high sensitivity, and good randomness. Moreover, an improved finite precision period calculation (FPPC) algorithm is proposed to calculate the repetition rate of the sequence and further discuss the relationship between the repetition rate and fixed-point accuracy; the proposed FPPC algorithm can be used to set the fixed-point notation for the proposed PRNG and avoid the degradation of the chaotic system due to the data precision.

Pengamanan M-Commerce Menggunakan One Time Password Metode Pseudo Random Number Generator (PRNG)

Mobile commerce atau m-commerce merupakan sistem perdagangan elektronik (e-commerce) yang menggunakan peralatan bergerak seperti telepon genggam, telepon pintar, PDA, dan notebook. Dengan pertumbuhan pengguna telepon pintar (smartphone) di seluruh dunia, banyak pemilik bisnis perdagangan elektronik yang juga menyediakan aplikasi m-commerce untuk mempermudah para pelanggannya dalam bertransaksi. Selain memberikan kenyamanan bagi pengguna, penyedia aplikasi m-commerce harus dapat memastikan bahwa pelanggan dapat bertransaksi dengan aman. Resiko keamanan merupakan salah satu kendala besar dalam perkembangan sistem perdagangan elektronik. Oleh karena itu, pada penelitian ini diterapkan metode pengamanan aplikasi m-commerce menggunakan one time password (OTP) yang dibangkitkan dengan metode Pseudo Random Number Generator (PRNG). Penelitian ini juga melakukan modifikasi terhadap algoritme PRNG dengan melakukan tiga kali proses bit shifting dan menambahkan algoritme enkripsi. Hasil pengujian menunjukkan bahwa sistem dapat membangkitkan OTP yang selalu unik untuk setiap transaksi. Hasil penelitian ini bermanfaat bagi pengembang aplikasi m-commerce untuk mengamankan aplikasinya.

Exploiting Random Chemistry for Digital Security

Randomness is such an important resource of our digital information era that new random number generators are invented continuously. Typically, these are electronic devices. Recently, researchers of the Universities of Glasgow and Edinburgh devised a novel method using stochastic chemical reactions.

Secure Collaborative Key Management System for Mobile Cloud Data Storage

Mobile Cloud Computing (MCC) is the combination of mobile computing, cloud computing and wireless networks to make mobile thin client devices resource-rich in terms of storage, memory computational power and battery power by remotely executing the wide range of mobile application’s data in a pay-per-use cloud computing environment. In MCC, one of the primary concern is the security and privacy of data stored in cloud. The existing techniques are not efficient to manage secret keys during key generation and key distribution processes. The objective of this project work is to develop a secure collaborative key management system (SCKMS) for mobile cloud data storage by implementing by the cryptographic techniques for file encryption and file decryption, key generation, key encryption, key distribution and key decryption processes. In our proposed methodology, DriverHQ public cloud infrastructure is used for accessing the secure file as Storage as a Service (SaaS) mechanism. For generating the secret key, the proposed work implemented with Pseudo Random Number Generator (PRNG) algorithm, it produces the sequence of random numbers for every time. The keys are distributed using general Secret key Sharing Scheme (SSS). The key pattern matching process is implemented to spilt the secret key into three partitions and sent it to client (mobile devices), cloud server and decryption server. The decryption server key and cloud sever key are mapped with client key. The key shares are grouped together using key-lock pair mechanism and it achieves key integrity during untrusted medium communication. The proposed work also eliminates key escrow and key exposure problems. The files are encrypted and decrypted using Rivest-Shamir-Adleman (RSA) algorithm. The RSA algorithm is more vulnerable against the brute force attack, because of using larger key size. Thus, the proposed SCKMS achieves data confidentiality and data integrity in mobile cloud storage data when compared to existing Key Management System (KMS). The work also reduces encryption & decryption computation and storage overhead in client mobile devices, and minimizes the energy consumption of the mobile devices efficiently.

Вихревой генератор стохастических плоскостей

Various pseudorandom number generation algorithms may be used to create a discrete stochastic plane. If a Cartesian completeness property is required of the plane, it must be uniform. The point is, employing the concept of uncontrolled random number generation may yield low-quality results, since original sequences may omit random numbers or not be sufficiently uniform. We present a novel approach for generating stochastic Cartesian planes according to the model of complete twister sequences featuring uniform random numbers without omissions or repetitions. Simulation results confirm that the random planes obtained are indeed perfectly uniform. Moreover, recombining the original complete uniform sequence parameters allows the number of planes created to be significantly increased without using any extra random access memory.

A novel pseudorandom number generator based on pseudorandomly enhanced logistic map

In last years, low-dimensional and high-dimensional chaotic systems have been implemented in cryptography. The efficiency and performance of these nonlinear systems play an important role in limited hardware implementations. In this context, low-dimensional chaotic systems are more attractive than high-dimensional chaotic systems to produce the pseudorandom key stream used for encryption purposes. Although low-dimensional chaotic maps present some security disadvantages when they are used in cryptography, they are highly attractive due its simple structure, discrete nature, less arithmetic operations, high output processing, and relatively easy to implement in a digital system. In this paper, we proposed both a pseudorandomly enhanced logistic map (PELM) and its application in a novel pseudorandom number generator (PRNG) algorithm, which produces pseudorandom stream with excellent statistical properties. The proposed PELM is compared with logistic map by using histograms and Lyapunov exponents to show its higher benefits in pseudorandom number generator. In contrast to recent schemes in the literature, we present a comprehensive security analysis over the proposed pseudorandom number generator based on pseudorandomly enhanced logistic map (PRNG–PELM) from a cryptographic point of view to show its potential use in secure communications. In addition, the randomness of the PRNG–PELM is verified with the most complete random test suit of National Institute of Standards and Technology (NIST 800-22) and with TestU01. Based on security results, few arithmetic operations required, and high output rate, the proposed PRNG–PELM scheme can be implemented in secure encryption applications, even in embedded systems with limited hardware resources.

Two Improved Pseudo-Random Number Generation Algorithms Based on the Logistic Map

In this study, we improve the pseudo-random number generation algorithm of Java based on the logistic map. We replace the seed of the random generation algorithm with a sequence of numbers which is generated by the original logistic map and the improved logistic map. And then we describe the two improved algorithms in detail including the essence of two algorithms and the processes. Also we display a series of experiments to prove the improved algorithms have a good randomness and they are efficient. Eventually we conclude our study and list our future study. The two improved pseudo-random number generation algorithms take advantage of the chaotic characteristics of the logistic map which makes it become much more efficient.

Polynomial pseudo-random number generator via cyclic phase

Fast and reliable pseudo-random number generator (PRNG) is required for simulation and other applications in scientific computing. In this work, a polynomial PRNG algorithm, based on a linear feedback shift register (LFSR) is presented. LFSR generator of order k determines a 2 k − 1 cyclic sequence period when the associated polynomial is primitive. The main drawback of this generator is the cyclicality of the shifted binary sequence. A non-linear transformation is proposed, which eliminates the underlying cyclicality and maintains both the characteristics of the original generator and the feedback function. The modified generator assures a good trade off between fastness and reliability and passes both graphical and statistical tests.

The Rise of Computationally Enabled Economics: Introduction to the Special Issue of the Eastern Economic Journal on Agent-Based Modeling

The first digital computers were primarily used by scientists and engineers to solve mathematical equations numerically, that is, to approximate analytical solutions, most commonly for difficult-to-solve differential equations. The economics profession was also an early adopter of digital computing, and many of the first uses of computation by economists involved numerical solution of economic equations that were hard or impossible to solve analytically. In this, the computer served as a powerful calculation engine while the problems being solved were conceptually similar to those that had come before, that is, constrained optimization. Much of this work had a heavy normative flavor, as companies had great financial incentive to learn how to better manage their inventories, optimize assembly lines, forecast demand for their products, and so on. Indeed, Herbert Simon, one of the early pioneers in this area, was fond of recalling that his work on dynamic programming in the 1950s taught companies how to better manage their production processes. It was very clear to that generation of economists that firm behavior was far from optimal, a fact that, Simon lamented, later generations seemed to have forgotten. In his Machine Dreams: How Economics Became a Cyborg Science, Philip Mirowski (2001) masterfully charts the early adoption of digital computer technology by economists and closely affiliated researchers (in game theory and operations research primarily, including many refugees from physics). Indeed, as operations researchers learned to solve wide classes of optimization problems computationally, through so-called mathematical programming methods (e.g., linear programming, integer programming, dynamic programming), economists were early users of such techniques for practical and policy problems. Monte Carlo methods were systematized early-on in the digital era, primarily for their use in evaluating difficult to solve integrals. This led to the development of reasonably sophisticated pseudo-random number generation algorithms and the rise of modern simulation techniques. Already in the 1950s, the so-called microsimulation was being used by economists and policy makers as a way to better forecast and understand alternative economic policies (Orcutt et al. 1961). This use of computation was somewhat different from the mathematical programming that had come before. While still involving optimization, such calculations were done at

擬似乱数生成アルゴリズムMersenne Twister によるMCNPの高速化と長周期対応

擬似乱数生成アルゴリズムMersenne Twister によるMCNPの高速化と長周期対応

Two-Dimensional Cellular Automata for Pseudo-Random Pattern Generators and for Highly Secure Stream Ciphers

Pseudo-random properties of a class of two-dimensional (2-D) 5-neighborhood cellular automata (CA), built around nonlinear (OR, AND) and linear (XOR) Boolean functions are studied. The site values at each step of the 2-D CA evolution are taken in parallel and form pseudo-random sequences, which satisfy the criteria established for pseudo random number generator (PRNG): long period, excellent random qualities, single bit error propagation (avalanche criteria), easy and fast generation of the random bits. A block-scheme for secure Stream Cipher based on 2-D CA is proposed. The 2-D CA based PRNG algorithm has simple structure, use space-invariant and local interconnections and can be easily realized in very large scale integration or parallel optoelectronic architectures.