• The KIPS Transactions:PartC
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• v.17C no.1
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• pp.47-60
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• 2010

Randomness is a basic security evaluation item for the most cryptographic algorithms. NIST has proposed a statistical test suit for random number generators for cryptographic applications in the process of AES project. However the test suit of NIST is customized to block ciphers which have the same input and output lengths. It needs to revise NIST's test suit for key derivation functions which have multiple output blocks. In this paper we propose a revised method of NIST's statistical randomness test adequate to the most key derivation functions and some experimental results for key derivation functions of 3GSM and NIST.

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.476-481
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• 2015

We have parallelized the cumulative sum (CUSUM) test of NIST's statistical random number test suite in a CUDA environment. Storing random walks in an array instead of in scalar variables eliminates data dependence. The change in data structure makes it possible to apply parallel scans, scatters, and reductions at each stage of the test. In addition, serial data exchanges between CPU and GPU are removed by migrating CPU's tasks to GPU. Finally we have optimized global memory accesses. The overall speedup is 23 times over the sequential version. Our results contribute to improving security of random numbers for cryptographic keys as well as reducing the time for evaluation of randomness.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.41-45
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• 2002

From the paper which it sees a strong random number generator it uses a fuzzy set from 16 method of the statistical test which is a cryptograph random number test it verifies. 16 statistical test of NIST extends in crptograph and engineering whole it is a scale which is important distinguishes the distinction incapable characterstic of the random numbers which are used. To try introduce a fuzzy set the possibility of having a more strong randomness in order to be, it strengthens the function of the random number generator.

• Journal of information and communication convergence engineering
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• v.8 no.5
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• pp.539-543
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• 2010

We present a random bit generator that uses fingerprint image for the source of random, and random bit generator using fingerprint image for the randomness has not been presented as yet. Fingerprint image is affected by the operational environments including sensing act, nonuniform contact and inconsistent contact, and these operational environments make FPI to be used for the source of random possible. Our generator produces, on the average, 9,334 bits a fingerprint image in 0.03 second. We have used the NIST SDB14 test suite consisting of sixteen statistical tests for testing the randomness of the bit sequence generated by our generator, and as the result, the bit sequence passes all sixteen statistical tests.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.2
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• pp.755-759
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• 2012

In this paper, it is about random number generator, which is based on hardware is utilized in medical science and game area. The Intel presents guideline of security level about hardware based true random number generator. At hardware based random number generator, the various test items, that are included in test suits as NIST statistical test, FIPS140-1, is applied. In this paper, it experiments about degree extent of randomness variation from filter scheme effects, which is applied in output stream of hardware noise source.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.77-86
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• 2024

Using random numbers to represent uncertainty and unpredictability is essential in many industries. This is crucial in disciplines like computer science, cryptography, and statistics where the use of randomness helps to guarantee the security and dependability of systems and procedures. In computer science, random number generation is used to generate passwords, keys, and other security tokens as well as to add randomness to algorithms and simulations. According to recent research, the hardware random number generators used in billions of Internet of Things devices do not produce enough entropy. This article describes how raw data gathered by IoT system sensors can be used to generate random numbers for cryptography systems and also examines the results of these random numbers. The results obtained have been validated by successfully passing the FIPS 140-1 and NIST 800-22 test suites.

• The KIPS Transactions:PartD
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• v.10D no.3
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• pp.541-546
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• 2003

We present a random bit generator that uses fingerprint image as the source of random, and the random bit generator is the first generator in the world that uses biometric information for the source of random in the world. The generator produces, on the average, 9,334 bits a fingerprint image in 0.03 second, and the produced bit sequence passes all 16 statistical tests that are recommended by NIST for testing the randomness.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.2
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• pp.327-334
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• 2016

Contrary to the common belief, DRAM which is used for the main memory of various computing devices retains its content even though it is powered-off. Especially, the data-retaining time can increase if DRAM is cooled down. The Cold Boot Attack, a kind of side-channel attacks, tries to recover the sensitive information such as the cryptographic key from the powered-off DRAM. This paper proposes a new algorithm which recovers the AES key under the symmetric-decay cold-boot-attack model. In particular, the proposed algorithm uses the strategy of reducing the size of the candidate key space by testing the randomness of the extracted AES key bit stream.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.6161-6176
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• 2018

Pseudorandom numbers are useful in cryptographic operations for using as nonce, initial vector, secret key, etc. Security of the cryptosystem relies on the secret key parameters, so a good pseudorandom number is needed. In this paper, we have proposed a new approach for generation of pseudorandom number. This method uses the three dimensional combinational puzzle Rubik Cube for generation of random numbers. The number of possible combinations of the cube approximates to 43 quintillion. The large possible combination of the cube increases the complexity of brute force attack on the generator. The generator uses cryptographic hash function. Chaotic map is being employed for increasing random behavior. The pseudorandom sequence generated can be used for cryptographic applications. The generated sequences are tested for randomness using NIST Statistical Test Suite and other testing methods. The result of the tests and analysis proves that the generated sequences are random.

• Journal of IKEEE
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• v.24 no.1
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• pp.52-58
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• 2020

This paper describes a hardware implementation of a true random number generator (TRNG) for information security applications. A new approach for TRNG design was proposed by adopting random transition rules in cellular automata and applying different transition rules at every time step. The TRNG circuit was implemented on Spartan-6 FPGA device, and its hardware operation generating random data with 100 MHz clock frequency was verified. For the random data of 2×10⁷ bits extracted from the TRNG circuit implemented in FPGA device, the randomness characteristics of the generated random data was evaluated by the NIST SP 800-22 test suite, and all of the fifteen test items were found to meet the criteria. The TRNG in this paper was implemented with 139 slices of Spartan-6 FPGA device, and it offers 600 Mbps of the true random number generation with 100 MHz clock frequency.