Summaries of A Cryptographic Algorithm Based on a Pseudorandom Number Generator

In this article, HENKOS, a new algorithm that is cryptographic is proposed along with a new number generator that is pseudorandom on the basis of which, the algorithm is created which appears quite fast and secure. The designing of this algorithm was carried out for fulfilling goals like the absence of short cycles, and cryptographically security etc. The cryptosystem actually uses two keys a DK or data key and MK or master key. It has been described that the results included ease of implementation, an algorithm that was cryptographically secure, an algorithm that was quite fast, and a generator of the pseudorandom number which was quite fast. In the article, the technologies include HENKOS algorithm and PRNG. The merits in the articles discussed include the Diehard battery test, NIST statistical test, ENT tests, and swiftness of the algorithm. The limitation concerned the adjusting of PRNG: SHA1 for comparison.

Pseudo Random Number Generation Applied to Robust Modern Cryptography A New Technique for Block Ciphers                      

The article discusses the vulnerability of the encryption systems and why it is the need of time to evolve towards better and dependable systems that not only are protected in terms of hardware but international hacking attacks. The paper has proposed the method to protect hardware of the cryptosystems but the integration of embedded systems of two functions that work on two key elements of online error detection and misleading corrupt results. Next-bit statistical test is applied in the research study. The authors have in detailed explained the development of reliable cryptosystems.  Not only the development and key features are discussed but the implementation process and the proposed strategy has been comprehensively explained. Advanced optimization of pseudo-random number generator algorithms can help achieve a secure cryptosystem as the inclusion of complete randomness can assure full protection against international attacks.  In the article, the limitations include Round-replica and PRNG properties. The new methods of round-based block ciphers are discussed in the article making it knowledgeable and helpful in understanding the protection of cryptosystems.

New Design of Crypto-Based Pseudorandom number generator (CBPRNG) using BLOWFISH cipher                             

The role of Random Number Generators is discussed in the paper that how they produce the sequence of zero and one bits to be formed and combined into subsets of the random number.  Random Number Generators are a core to the working algorithms and protocols in the cryptography. The paper elaborates how repetitive results are obtained from running the same random generator twice. Some of the basic statistical tests like NIST, ENT, TU01, and Dihard can be used in the research. The paper proposes a system that uses the BLOWFISH ciphers technology that along with Cipher-block chaining used three stages of the block chaining. The overall use of this method increases the efficiency of the system and the security of the system. The proposed generator uses three 3DES with a combination of two keys. BLOWFISH ciphers are faster. The limitation although concerned the CBC’s excessive process timing. The final result by the CBPRNG shows no repletion of results showing the efficiency of the system and how it is the right approach in developing new and secure cryptosystems.

On Pseudo-random Number, Generation Using Elliptic Curve Cryptography

The paper tells us about the algorithm of using Elliptic curve cryptography for Pseudo-random number generation algorithms. It distinguishes the proposed method and its advantages over the other methods.  One advantage of ECC schemes include the basis of it on the public key mechanism. This mechanism ensures encryption and other added benefits such as key exchange algorithms.  The numbers generated are totally random and require a key. The paper tells us that Elliptic curves are famous for their best performances and the algorithms they use.  Though the method is not a new one but refining it with new algorithms can help achieve a different level of efficiency. NIST statistical test is also used in this paper to analyze the algorithm working. The article compares the results of existing and proposed algorithm where the final results reveal the using the proposed algorithm provides better security in comparison with the existing one.  The article is concluded by informing that random numbers are securely generated by the proposed algorithm. The limitation of this study concern the whole sequence’s identification.

Pseudo-random number generator based on the mixing of three chaotic maps

The 9-page article serves to be a knowledgeable piece on a different approach of secure pseudo-random number generator. It tells us about using a mixture of three chaotic maps generated from the input initial vector. The paper uses different terms that make us understand the concept and its implementation which helps to analyze and differentiate from the existing and other proposed generators.  Random evaluation sequences are appraised via NIST statistical tests. The system is based on touch points from the relevant data and generation of possibilities to design a more secure and viable system that is not vulnerable to global and international attacks. As the algorithm is based on three chaotic maps input it is more complex and the numbers generated are totally random and secure. The authors have discussed the different tiers of the system and how the system is more secure against several attacks and despite the complexity, it is easy to implement and understand. The limitations of this research revolve around the fact that the only algorithm was focused upon and other technologies were not paid attention to.

A True Random-Number Encryption Method Employing Block Cipher and PRNG

The article discusses the fact that how the older systems have been obsolete and not secure which is why it is needed to innovate and introduce new systems based on the available knowledge and understanding of the dynamics. As the world is more online and using the internet it is making the networks and users more vulnerable to the attacks by the hackers which are making the data security a growing concern. The older methods are not helping and a new method is proposed which is more random and tier based security which ensures network-wide security in cloud and hardware. The True Random Number Encryption algorithm employing Block Cipher and PRNG is proposed in this paper. NIST and ENT test can be used in the Random Number Encryption algorithm It explains the working and implementation with the analysis of the result given showing how incorporation of different layers make the networks more secure and make the proposed method favorable due to its reliability. This study has limitations and they concern the first secret parameter’s setting on microprocessors in a secure way.   

Pseudo Random Number Generator and Hash Function for Embedded Microprocessors

Internet of things is the main concern in the article and it compares different technologies focusing on the algorithms needed for microprocessors.  The paper focuses on the development of Pseudo Random Number generators for the microprocessors in use of internet of things as the microprocessors have limited computing powers which require better and new innovative ways to accommodate the Internet of things.  The authors have proposed the use of Hash function and Pseudo Random Number Generator algorithm for embedded microprocessors.  The proposed method is evaluated using different criteria to gauge the efficiency of the new systems. It is concluded that the method requires minimal ROM and RAM storages as is most viable for the Internet of Things application. The random sequence is evaluated on the NIST test suite. There is a lot of potential in the method to be used for sensor networks and RFID. It uses a simple algorithm that is able to perform complex operations without using much of computing power. The limitations of this study concern the fact that keys were not paid enough attention to that the encryption method generated.

Pseudorandom Bit Generator with Parallel Implementation

This article by Stoyanov and Kordov technology focuses on different aspects of using pseudo-random bit generator coupled with parallel implementation. It lays emphasis on the algorithm that works to address the issue of low memory availability and distributes the processes by the division of dynamic date blocks for encryption over different cores of the processors. The shrinking of the data block is inevitable due to less available memory. The generated bit streams security is proven by using DIEHARD, NIST and ENT statistical testing. The proposed method is backed with statistical data and its analysis to support the argument of the authors. Also, the data helps us analyze the performance and time difference which serves as a benchmark when comparing the other methods. The proposed method combines the single feedback with the carrying shift register and the Editing bit search rule. In the research, only advantages are discussed and The limitation of using these algorithms is that attacks beside the editing bit-search rule functional on LFSR can turn to be unrealistic in the new mutual system. The algorithm follows the integers that define the feedback taps. Regularity check is the last check of the follow up to ensure consistency in the generated random numbers.

A novel method for producing pseudo-random numbers from differential equation-based chaotic systems

The Journal article discusses the possibility of using an algorithm of the hybrid system to generate pseudo-random numbers algorithms based on two different and distinct approaches. The method is based on different parameters value by switching to produce pseudo-random numbers from chaotic systems. It ponders over the transition of different chaotic equations by using the linear feedback shift registers.  The pseudo-random numbers formed are tested by using TestU01and NIST SP 800-22 test suites that are passed. The article elaborates the mechanism and different algorithms that will be used and not only this it provides in-depth analysis of the results obtained due to the implementation of the proposed system.  It is concluded that the shifting method based number generators are efficient and its throughput rate is higher. The distribution of data is uniform which refers to the consistency of the data provided and results from the implementation. The limitations are that in these algorithms the chaotic maps undergo from partial keyspace, and low dimensions offer weak safety measures. The authors discuss the different possibilities of using the proposed methods in different applications such as image encryption which may require pseudo-randomness.

Fast Implementation of Block Ciphers and PRNGs for Kepler GPU architecture

The article discusses the role of GPU in modern day technology and how the overall inflow of data has increased with the Internet of things and cloud computing. The article tells the importance and needs for a more secure online environment.  The papers propose the method to make the operations of GPU more secure and hack free to ensure security. The paper shares the result of the implementation of different methods and techniques on NVIDIA GTX 600 GPU with Kepler technology to serve as a benchmark to draw a conclusion for further research and implementation.  The Blowfish and IDEA pass NIST and TestU01 Statistical Test apart from stringent tests in TestU01. The three-block cipher methods used as stated in the paper are BLOWFISH, Three Fish and IDEA.  The article concludes by comparing the results and establishing that block ciphers are used for fast implementation of secure encryption. The limitation of algorithms is that execution rounds are limited to eight performance rounds. However, it is also discovered by the analysis of speed and benchmarks obtained that bandwidth is an important variable in it. 

High Speed and Secure Variable Probability Pseudo True Random Number Generator using FPGA               

This article focuses on different aspects and applications of the random numbers generators and where and how they are used.  Pseudo and True Random Number generators algorithm work on two different distinct principles based on mathematical properties. The article discusses the probability of different outputs and how they may not be equal depending on the algorithm that alters the probability and output produced.  The application with respect to banks and game development is discussed. For randomness testing, it uses the NIST Statistical Test Suite. It is an informative piece discussing having a diverse and different approach when dealing with different sorts of conditions. PRNG is less secure than TRNG but the throughput speed is superior than it is discussed in the article. The paper has presented different ideas and how generators have been used on the proposed lines to display secure characteristics by permitting trustful logging. The limitations of the PRNG are that it is less protected than second. A personalized Pseudo Random Number Generator based on linear feedback shift register cascade is proposed.

Pseudo-random number generator based on the generalized Lorenz chaotic system 

This paper discusses the growing phenomena of adapting the chaos in different fields of science and proposes to use the method of pseudo-random number sequences from a single generalized Lorenz system.  The article proposes a new algorithm that encompasses different variables based on the Lorenz chaotic system differential equation. The new proposed system is more secure based on the analysis of comparison of different pseudo-random number generators and the benchmarks.  In this paper, NIST SP800-22 tests are used. The article sheds light on the two new proposed algorithms and includes statistical analysis of the test implementation of the proposed systems. To conclude the article the two algorithms are used to generate the binary sequence where one uses the sum of three coordinates whereas the other one uses the chaotic orbit of the three coordinates based on the GLS. The limitations concern the personalized cascade of LFSR. The proposed PRNG can be used as a source to generate pseudo-random numbers being used in cryptography.

Comparison of some cryptography algorithms and generations of Pseudo-Random number by using a pattern of general evaluation

Pseudo-random algorithms are used in several areas such as designing games, modeling, stimulation, communication channels especially cryptography which is highly versatile. There has been great research on pseudo number generation whereas some focus upon chaotic maps which are used for cryptography. Despite research, not all pseudo number generators can be used as a cryptography algorithm. The CAST-256 is algorithm is introduced which has 128 as the block length as well as 128, 160, 192, 224 & 256 as the variable keys. One of the drawbacks of cryptography is its lowest speed. SALSA algorithm is one of the types of the steam algorithm, which has 256 bits as key length and ARX is the basis of its structure and is of quality with regards to security & speed.

Algorithms are important therefore its investigation, accuracy and values should be properly analyzed. NIST statistical test is employed in this paper for the Removal of an investigation stage can lead to cause errors. This paper includes investigation of block and stream ciphers which suggest that AES256, MT19937and Salsa is blocked, cryptographer. The limitation of the algorithm is the concern with limited information and LFSR’s limited iterations. A stream cryptographer and pseudo number generator have passed all tests and are able to get privacy certificate.

Pseudorandom Number Generation: Efficient deterministic as well as non-deterministic

 A pseudorandom number is one of the generators (PRNG) algorithm helps to generate pseudo numbers in a pattern of deterministic and valuable way. The high performance, as well as high-quality pseudo number generated, is generated and brought under discussion. Generation of one pseudorandom byte requires one cycle of the clock on Intel core i3 processor and pass through 6TestU01 batteries of the test.  These generators have the capacity to work in two functional modes. 1) Deterministic mode 2) Non- deterministic mode

The deterministic model is useful for data encryption with high speed and several another kind of applications which utilize reproducible pseudorandom sequences and deterministic mode. When the non-deterministic mode is used the generator begins to act like true random number generator. But this mode has several advantages such as high performance, lower cost, and availability. The non-determinant mode is good and which depend upon true random generators.

The pseudo number generator based on the word can be used in software, and they can be effective in a 64-bit processor. TestU01suite can be utilized as a most statistical test suite to understand random sequences. Apart from deterministic, the non-deterministic number generation MaD1 also has the capacity to work in this given mode. The limitations concern the fact that TRNGs are not adopted as they are slow and expensive as well. It can be used as a random number generator in several applications.

Evolving cryptographic pseudorandom number generations

In real-world random number, generators play a very important role. Apart from a hardware random number generators, the most important class is deterministic random number generators algorithm. These generators lack predictability of RNGs. Deterministic RNG should be used in cryptography as it fulfills all the requirements related to security, speed, and ease of implementation. DIEHARD and NIST test suites are mainly implemented in this study. The results which are obtained suggest that Cartesian Genetic Program (CGP) can be used as a suitable choice for the evolution of Pseudo Random Number Generations (PRNG). In order to understand a real-world example of such generators, the limitations of PRNGs should be known and understood so there can be a better understanding of what properties are needed. Moreover, it presents a function which is appropriate in several matters as per presented before. Emphasis is upon the development and evolution of PRNGs which are extremely fast and small and do not depend upon expensive operations of addition or multiplication. The limitations concern the fact that PRNG’s inner working is not mimicked by fitness functions.

Key requirements for the design of robust chaotic PRNG

With the growing usage of the electronic medium, there should be more innovative schemes of security so the information can be transferred in a more secure way and transmission and storage are secured. The method of encryption is required to specify for each transaction, therefore, new generator keys are required and for the purpose, chaotic keys look like perfection in such a moment of need. Ring coupling algorithm is used for the generation and robust of a generator. The chaotic keys pass all the statistical and analytical tests which are NIST, autocorrelation, cross-correlation, Lyapunov exponent and uniform distribution.  For precise uniform distribution approximate density function is applied to it and if there are any errors these are identified by the software. The resultant chaotic system ensures designed robust implementation on cryptosystem. Chaotic maps suggest that if there are any weak cryptographic features, these will cause performing chaotic pseudo-random numbers generator which in turn produces excellent cryptographic properties. The major limitation of the research is that there is only 2D system are describe and other methods are not discussed in comparison with the 2D systems.

Comparative analysis of SLA-LFSR with traditional pseudo-random number generators

Production of the random number generated is an important tool for the generation of cryptographic keys but security is also necessary for it and for algorithm steps game theory, stimulation, and the statistic is very important in cryptography. Random number generator is an algorithm which is used to produce unpredictable and random keys. Different security aspects and performance are compared to LCG, LFSR, BBS, and SLA-LFSR. It is found that pseudo-random number generators are very important and require it for cryptography and if it is compromised the entire system of cryptosystem is destroyed. Security systems are supported by the design of PRNG. NIST statistical test is employed in this study of LCG, LFSR, BBS, and SLA-LFSR. The starting time should be less and frequent initialization is required for effective functioning of PRNG. Among all the generators SLA-LFSR is the optimal PRNG because of optimal memory, long period of time and usage of CPU in order to provide effective and efficient security system. The limitations concern the repeatability and unpredictability of pseudo-random generator of numbers.

(Pseudo) Random number generation on the basis of Source of Computer

Random numbers play the analytical role in cryptographic fields. This can be utilized in seed, cryptographic key, nonce etc. Random numbers are used for the initialization of generation of a pseudo number and then generate arbitrary keys. The statistical test suite such as NIST is used for the checking of generated pseudo-random numbers. The hardest part of the cryptography is its key management. On the basis of random sources of PC, RNG and PRNG algorithm are suggested. RNG generates the random key of 128-256 bit from random sources of PC. The statistical analysis is used to check the generated random keys. All of the keys are checked in cumulative, clock frequency as well as frequency test. And all of these keys gave 88% result upon checking. On the other hand, PRNG, pass all the tests with distinction and give 100% result in the test. The results suggest that random keys are reliable and useful for generation of results while PRNG algorithms are reliable and give security of transferred information. The limitations concern the generation of imbalance sequences by BBS.

Methods for Implementation of pseudo-random number generator on the basis of GOST R 34.12-2015 in hybrid CPU/ GPU/ FPGA high-performance systems

The design, management, and working of high-performance data storage and processing systems have changed noticeably. The cloud computing systems are not only hybrids but are capable of supporting hardware acceleration as well. The work of this article provides knowledge of implementation in the hybrid system of GOST R 34.12-2015 based pseudo-random number generator. PRNG solve the great variety of tasks greatly depending upon the features. The basic statistical method can be used in the case for the testing the debugging program, the NIST and DIEHARD can be the most preferred statistical test to be used. In recent time, the high-performance processing systems as well as data storage, both have changed to a greater extent. The stochastic conversion's multidimensional algorithms are used for GPGPU systems with high performance. The higher degree of parallelism allows Grasshopper algorithm to offer multithreaded systems. For any special purpose which is used in EUC, the hardware approach of implementing Grasshopper GOST R 34.12-2015 is one of the approaches of choice still being used. The limitations of this research concern reduced key spaces, nonrandom numbers, and poor choices of keys.

The generators of the new pseudorandom number from the block ciphers

Several cryptographic applications and protocols are used for security which is related to randomness. The pseudo-random number generators from ciphers possess HBC and HTR algorithms. These are secured in PRG-CIA sense with the thought of underlying block cipher is secured. HTR is considered to be a parallel structure and cascade structure is considered for HBC. HTR is highly efficient than HBC. Both HBC and HTR provoke the use of block cipher that comes with efficient performance results of ANSI X9.17 PRNG algorithms in implementing the software as well as hardware. Randomness is checked with the help of NIST test which gives out a perfect result and passes all the tests of statistics. PRNGs have a lot of applications. They are capable of using and securing cryptographic protocols, protect computations of several parties, ensure the end to end encryption and many other settings. In this study, the limitations are imposed critical necessities by cryptographic algorithms and intractability assumptions as well.

Random number generation of LFSR based stream cipher algories

This has several uses such as sampling, games of chances, simulation, computer science and function like cryptography, game programming and transmission of data. There are 3 basic requirements which should be fulfilled for using the random system in computer science.

The generated numbers should be unpredictable.

The generated numbers should have great statistical purposes.

And the numbers which are produced must not have the quality to reproduce.

The random numbers which are followed using above stated properties are categorized into two classes, Pseudo Random Numbers Generator (PRNG) and True Random Number Generators (TRNG). The kind of PRNG is utilized for the production of Stream Encryption Algorithms. Random numbers are generated with the help of stream cipher based methods then these methods are implied on FPGA hardware. For obtaining numbers test such as NIST is performed. Random numbers possess excellent statistical properties only non- linear combination generator method could not test and failed Frequency Block test. The limitations concern the passing of NIST statistical tests and FPGA based 60nm.

The comparative analysis as well as the study of some algorithms of the pseudo-random number generator

Different LCG PRNGS algorithms present different results in the NIST test. It is observed that over PRNGs, the performance of all the LCGs was poor. Somehow, LCG's Linear combination that can be defined as Wichmann-Hill PRNG, it has done well against the given NIST test. Few implementations such as WELL44497b, and WELL512a failed their serial test, on the other hand, MIXMAX PRNG performed well and passed the serial test.

All the other tests such as non-overlapping test, random excursion variant test, and random excursion tests were failed by the considered generators present in the article. The PR value of Non-overlapping template matching test was low with high chances of failure. Therefore, test results taken in this paper about none of the generators are liable to be used for applications of cryptographic. The limitations of this algorithms concern performance against tests’ battery prescribed in the NIST SP800-22rev.

The generation of Acoustic lightweight pseudo random number on the basis of cryptographically secure LFSR

An acoustic lightweight pseudo-random number generator algorithms like one is called SLA-LFSR-PRNG, it should be used which consume CPU capacity, less memory as well as adopts the strategies that are parallelization with multi-thread in order to the generation of huge random numbers by taking benefits of the gigantic parallel design of GPU and multi-core CPU. The cryptographically based generator has the ability to (LFSR) and all the entropy from given sound sources are driven out. One main thing of suggested PRNG is the protection to major attacks, which are being done on the pseudo-random number generators. A secure pseudo-random number generator (cryptographically) is immune to PRNG threats as well as utilized low capacity and memory. The generator is tested in statistical test suite of NIST SP 800-22 and then all the tests are passed and have significantly improved performance. The limitations include NIST SP 800-22 statistical test for the proposed generator's comparison on various system suite to comparison of the proposed generator on different systems.

Enhanced pseudorandom number generator based on Blum-Blum-Shub and elliptic curves

Blum-Blum-Shub (BBS) is not considered a complex PRNG and needs a squaring operation and large modulus for the generation or production of each bit making it slow computationally or heavy. The elliptic curve (EC) point towards point operations that have been extracted to the given PRNGS algorithm and hence prove that reduced latency and good randomness properties and show dependence upon the secrecy of P. The strength of BBS lies in IFP and PRNGs strength lies in DLP. Big modulus modulo requirement for BBS should be secure and slower in performance. NIST standard statistical test suite is adopted in this article. Dependence on P is high, which shows loopholes for PRNGs are present. The performance test showed that the confidence level is above 99.7% but randomness tests were all right and passed. The test results of the article show that BBS-ECPRNG is certainly a secure one, and statically produces some randomness, that is required for various practical application. In this study, the limitations of the algorithms surround the observation of performance sequences at a minimum confidence level.

The Performance of Blum-Blum-Shub Elliptic Curve Pseudorandom number generator as Wi-Fi protected access 2 with security key generator

The Wi-Fi Protected Access 2 (WPA2) is thought to be more secure security protocol for networks, which exists in routers that are wireless, although partial key vulnerability is exposed. The strength of BBS lies in IFP and PRNGs strength lies in DLP. This BBS-ECPRNG algorithm is used instead of algorithms in embedded routers. In order to prove the validity of such use validity was checked and tested by the help of NIST statistical test which proved that the generator is secured and generate randomness as required and this quality is essentially required in several cryptographic applications. The article showed that WPA2 passwords in routers were distributed in the Philippines. The research and suggestion show that BBS-ECPRNG is much more efficient and recommendable against WPA2 as it is random, secured and fast. The limitation of BBS-ECPRNG is that it’s generating unpredictable and random WPA2 composition passwords that are likely to decrease the success of password-cracking.

Sequence	Authors	Year	Focus	Technologies	Performance metrics	Limitations (If there)	Source
1	Dominic Bucerzan	2008	Introduction of a new cryptosystem on the basis of HENKOS, a synchronous stream cipher, developed around a generator that is performing keystream.	PRNG and HENKOS algorithm.	Fast algorithm ENT Tests NIST statistical test Diehard battery test.	2 PRNG: SHA1 were chosen for comparison.	IEEE

 

 

Sequence	Authors	Year	Focus	Technologies	Performance metrics	Limitations (If there)	Source
2	Alejandro Jiménez-Horas, Enrique San Millán, Celia López-Ongil, Marta Portela-García, Mario García-Valderas, Luis Entrena	2009	A brand new solution is introduced dealing with PRNG for misguiding attackers and improving the reliability of cryptosystem.	PRNG, BBS, and PRBG	Fault tolerance. Protection. AES-128 Time performance.	PRNG properties and Round-replica.	IEEE

 

 

Sequence	Authors	Year	Focus	Technologies	Performance metrics	Limitations (If there)	Source
3	T.Chalama Reddy, Dr.R.Seshadri	2013	A new random number generator that is pseudo on the basis of crypto.	PRNG, BLOWFISH chippers, and CBC	Encryption. Efficiency. Faster. Sufficient randomness.	The excessive processing time of CBC.	IEEE

 

 

Sequence	Authors	Year	Focus	Technologies	Performance metrics	Limitations (If there)	Source
4	Manali Dubai and Aaradhana Deshmukh	2013	Describing the deriving mechanism of random number and possibilities of RNG attack on algorithms of ECC. Proposing an algorithm to be utilized in producing random numbers.	Elliptic Curve Cryptography, RNG algorithms, and ECRNG algorithm	Secure generation. Security.	Identification of the whole sequence.	IEEE

 

Sequence	Authors	Year	Focus	Technologies	Performance metrics	Limitations (If there)	Source
5	Borislav Stoyanov And Krasimir Kordov	2014	The focus of the whole research and the article is on the pseudo-random bit generator with its parallel implementations. In the article, along with editing bit-search generator the carry shift register feedback is used to develop a new scheme known as "pseudorandom cryptographic". However, research was also focused to propose a derivative system that can ensure security in the digital ways of communication.	Algorithm, FCSR,	NIST DIEHARD ENT FCSR memory	Mainly focused on the algorithm rather than other available techniques also.	International Conference on Large-Scale Scientific Computing

 

Sequence	Authors	Year	Focus	Technologies	Performance metrics	Limitations (If there)	Source
6	Hwajeong Seo, Jongseok Choi, Howon Kim, Hyunjin Kim, Taehwan Park	2014	The main focus of the article was on Embedded Microprocessors. In the article, the purpose was to present the lightweight implementation for PRNG. The problem discussed in the article is that microprocessors are unable to provide enough computing power and storages that are required in the present age.	RNG, ECB(mode), Algorithm,  PRNG (2 Gigabytes),	NIST test (version 1.6), AES accelerators	The setting of the 1st secret parameter on the microprocessors (embedded microprocessors) insecure way was out of the paper's scope.	IEEE

 

Sequence	Authors	Year	Focus	Technologies	Performance metrics	Limitations (If there)	Source
7	Yi-Li Huang, Fang-Yi Leu, Jian-Hong Chen, William Cheng-Chung Chu,	2014	The main focus of studies was to develop a system or method through which we can limit the hackers from attacks. In the research, TRNEM was used to make the system more secure. The author conducted a research study by the use of TRNS to make the hackers unable to getting access and cracking the protected ciphertext.	Block Cipher mode of operations, CFB mode,  PCBC mode,	TRIM DES AES	In the article, author did not provide enough attention to the keys generated by the True Random Number Encryption method.	Computer Science and Information Systems

 

Sequence	Authors	Year	Focus	Technologies	Performance metrics	Limitations (If there)	Source
8	M. François T. Grosges D. Barchiesi R. Erra	2013	The article is written on the PRNG based on mixing of 3 major chaotic maps. In the article author, elaborated the performance of the scheme through the use of statistical analysis. In the article main focus is on the cryptographically secure PRNG as it can provide several advantages to the modern technologies like it can provide support through ensuing security again the possible external attacks and can ensure a large key space.	PRNG Algorithm	Correlation NIST	In the article, only advantages of PRNG are discussed while research should also present the disadvantages and limitations of the PRNG also.	Communications in Nonlinear Science and Numerical Simulation

 

Sequence	Authors	Year	Focus	Technologies	Performance metrics	Limitations (If there)	Source
9	Cheong Hon-Sang, Lee Wai-Kong,		The purpose of the article is to elaborate that how encryption/ decryption speed can be improved and enhanced in the GPU when a number of challenges are there. The article studied the implementation of the IDEA, Threefish, and Blowfish in the GPU with specifically Kepler architectures.	Block Cipher technology CTR Mode, PRNG, Cryptographic Algorithms, GPU  IDEA Threefish Blowfish	Memory NIST test suite, TestU01	No	IEEE

 

Sequence	Authors	Year	Focus	Technologies	Performance metrics	Limitations (If there)	Source
10	Ahmad Gaeini, Abdolrasoul Mirghadri ,Gholamreza Jandaghi ,Behbod Keshavarzi	2016	In the research, article focus was on selecting the algorithm that can be considered as the most secure algorithm therefore in the research researcher checked the capability through passing several algorithms from model stages and the one that was able to pass all was selected to receive privacy certificate. Several stages of the model include speed, through search, NIST first level, and sensitivity analysis.	Block Cipher encryption, Stream Cipher, Algorithm PRNG	Security Accuracy Speed sensitivity analysis   NIST test	Investigation sequence,  All the areas of research are not given equal importance.	I.J. Information Technology and Computer Science

 

Sequence	Authors	Year	Focus	Technologies	Performance metrics	Limitations (If there)	Source
11	Volodymyr Lynnyk, Noboru Sakamoto, Sergej Celikovsky,	2015	Key sensitivity of the PRNG algorithms and the key spaces are the two areas of study in the article for the security analysis of PRNG algorithm, TRNG as the apparatus to generate the random numbers is being used to as TRNG can work in the physical process rather than the computer programs. The article presented that how the implementation of the PRNG (concerning with the GLS) can make the combination more secure.  Paper mainly focuses on the approach that can generate sequences in the pseudo-random numbers through the support of GLS (Generalized Lorenz System). SP 800-22 (NIST test) use to find out the p-value and results of test index including cumulative run, frequency, and Random Excursions.	PRNG Algorithm TRNG	Security Sensitivity Correlation NIST SP 800-22 test	No	IFAC

 

Sequence	Authors	Year	Focus	Technologies	Performance metrics	Limitations (If there)	Source
12	Andrei Marghescu, Paul Svasta, Emil Simion,	2015	The article presents the Solution for variable probability numbers generator that mainly relies upon the True Random Number Generator (TRNG) and Pseudorandom Number Generator (PRNG). Linear feedback shift register cascade is used to provide personalized PRNG in the research article.	PRNG, TRNG,	NIST, Probability,	Personalized LFSR cascade	IEEE

 

Sequence	Authors	Year	Focus	Technologies	Performance metrics	Limitations (If there)	Source
13	Ismail Ozturk, Recai Kilic,	2015	Elimination of the undesirable effects caused by the temporal discretization is discussed in the article. While the main focus of the article is to study the use of parameter switching in producing the PRNG. Performance and security were related aspects are also discussed in the article to elaborate the right solution for the problems.	PRNG, (CCS-PRNG, Proposed PRNG), TRNG	Secure NIST SP 800-22 TestU01 Cross-correlation	Limited information is discussed, Limited iterations of LFSR discussed.	Nonlinear Dyn

 

Sequence	Author	Year	Focus	Technologies	Procedures Metrics	Limitations	Source
14	Jie Lia, Jianliang Zhengb, Paula Whitlockc	2018	High performance and high-quality pseudorandom number generator	Intel core i3 processor 6 TestU01 batteries	Deterministic mode or non-deterministic mode	TRNGs, are still not widely adopted for several reasons: too expensive; relatively slow	ELSEVIER

 

 

 

Sequence	Author	Year	Focus	Technologies	Procedures Metrics	Limitations	Source
15	Stjepan Picek, Dominik Sisejkovic2, Vladimir Rozic, Bohan Yang, Domagoj Jakobovic2, and Nele Mentens	2016	Random number generators (RNGs) Evolution	Cryptography, Cartesian Genetic Programming, Blum Blum Shub generator	Generating seeds, nonces, initialization vectors, deterministic methods	Fitness functions that are used in related work actually inappropriate since they do not mimic the inner working of a PRNG	Springer International Publishing

 

 

Sequence	Author	Year	Focus	Technologies	Procedures Metrics	Limitations	Source
16	Oleg Garasym, Ina Taralova, Rene Lozi	2016	a new robust, gigaperiodic and simple in implementation chaotic generator	chaotic generator, ring-coupling, RSA algorithm	NIST, largest Lyapunov exponent, autocorrelation, cross-correlation, uniform distribution	chaotic pseudo-random number generators appear as promising candidates: indeed, they combine at same time stochastic features such as unpredictability, and deterministic features, such as Repeatability.	IEEE

 

 

Sequence	Author	Year	Focus	Technologies	Procedures Metrics	Limitations	Source
17	Auqib Hamid Lone	2017	Comparative Analysis of SLA-LFSR with Traditional Pseudo-Random Number Generators	SLA-LFSR, Random Number Generator, BLUM BLUM SHUB	Cryptographic algorithms, the mathematical algorithm	the problem of factoring the Blum integers in polynomial time as factoring the Blum integers is a hard problem, and BBS usually generates imbalance sequences having the equal frequency of 0's and 1's	ResearchGate

 

 

 

Sequence	Author	Year	Focus	Technologies	Procedures Metrics	Limitations	Source
18	Ganiyev Salim Karimovich, Khudoykulov Zarif Turakulovich	2017	new (pseudo) random number generator (PRNG or RNG) based on computer’s source	Pseudo-Random Bit Generator, Bull Mountain	Initialization vector, cryptographic algorithms	reduced keyspaces, poor key choices, nonrandom numbers	IEEE

 

 

Sequence	Author	Year	Focus	Technologies	Procedures Metrics	Limitations	Source
19	Andrey. A. Skitev, Mikhail M. Rovnyagin, Ekaterina N. Martynova, Marina I. Zvyagina, Kirill D. Shelopugin, Anastasia A. Chernova	2015	Method for implementing GOST R 34.12-2015 Based Pseudo-Random Number Generator in hybrid systems	GOST R 34.12-2015, queuing systems	Monte-Carlo method, simulation modeling	Cryptographic algorithms impose high requirements on the computing power of computers. There are	IEEE

 

Sequence	Authors	Year	Focus	Technologies	Performances metrics	Limitations	Sources
20	1). Ping Zhang, 2)Honggang Hu, 3). Xianjun Hu, 4).Xiaolong Yang	2017	1). Backdoored PRNGs with input, provided a formal definition 2). discrete logarithm problem	Algorithm(HTR, HBC) PRG-CIA PRNG	1). HTR better speed then HBC 2).HTR, HBC usage is secure in Cryptographic protocols. 3). Secure encryption, and multiparty computations	Intractability assumptions, such as the discrete logarithm, factoring, syndrome decoding 2). Permutations	IEEE

 

 

Sequence	Authors	Year	Focus	Technologies	Performances metrics	Limitations	Sources
21	1). Taner Tuncer 2) Erdinç Avaroğlu	2017	1).Generation of random number 2). Stream clipper algorithm based on LFSR	1).  Stream cipher-based methods 2).  Stream Encryption algorithms 3). LFSR based stream encryption algorithms	1). PRNG is cheap 2). Easy to implement 3). Stored in a memory unit	1). FPGA based 60-nm EP4CE115F29C7 2). NIST statistical tests random number passed from it	MIPRO

 

Sequence	Authors	Year	Focus	Technologies	Performances metrics	Limitations	Sources
22	1). Shobhit Sinha 2) SK Hafizul Islam 3). Mohammad S. Obaidat	2018	1). Statistical nature pseudorandom Number generators of the algorithm 2). discrete logarithm problem	1). Linear Congruential Generator 2). Wichmann-Hill Algorithm 3). Well Equidistributed Long Period Generator 4). MIXMAX.	1). Cryptographically is Secure  2). PRNG is not secure	Performance against a battery of tests prescribed in NIST SP800-22rev	WILEY

 

Sequence	Authors	Year	Focus	Technologies	Performances metrics	Limitations	Sources
23	1). Mohammed Abdul Samad AL-khatib 2 Auqib Hamid Lone	2018	1). Proposed secure, lightweight acoustic pseudo-random number generator 2). Algorithm PRNG	1). Algorithm (SLA-LFSRPRNG) 2). GPU architecture .	1). Used LFSR Cryptographically 2). Very secure 3). Consume Less memory	NIST SP 800-22 statistical test suite to the comparison of the proposed generator on different systems.	MECS Press

 

Sequence	Authors	Year	Focus	Technologies	Performances metrics	Limitations	Sources
24	1). Challis D. Omorog 2 Bobby D. Gerardo 3). Ruji P. Medina	2018	1). Improved Pseudorandom number generator	1). Algorithm (BBS) 2). Elliptic Curves 3). Algorithm PRNG	1). PRNG is cheap 2). Very secure 3). Consume Less memory	The performance sequences are observed at the minimum confidence level of 99.7%	IEEE