Genetic programming-based pseudorandom number generator for wireless identification and sensing platform

Kösemen, Cem; Dalkılıç, Gökhan; Aydın, Ömer

doi:10.3906/elk-1710-155

Cited by 12 publications

(10 citation statements)

References 25 publications

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“…50, it resulted in a 192-bit output and passing Cumulative Frequency, Gap and Chi-square tests. Recent work in the field by Kosemen 36 , which is an extension to the work carried out by Koza, uses evolutionary parameters of {popsize: 50, mutation: 0.05} with elitism, resulted in the design of CSPRNG which passed all NIST tests and produced output within 0.24960s resulting in 49 crossovers over 3 generations.…”

Section: Discussionmentioning

confidence: 99%

Design of a Cryptographically Secure Pseudo Random Number Generator with Grammatical Evolution

Ryan

Kshirsagar

Vaidya

et al. 2021

Preprint

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This work investigates the potential of evolving an initial seed with Grammatical Evolution (GE), for the construction of cryptographically secure (CS) pseudo-random number generator (PRNG). We harness the flexibility of GE as an entropy source for returning initial seeds. The initial seeds returned by GE demonstrate an average entropy value of 7.920261600000001 which is extremely close to the ideal value of 8. The initial seed combined with our proposed approach, control_flow_incrementor, is used to construct both, GE-PRNG and GE-CSPRNG.The random numbers generated with CSPRNG meet the prescribed National Institute of Standards and Technology (NIST) SP800-22 requirements. Monte Carlo simulations established the efficacy of the PRNG. The experimental setup was designed to estimate the value for pi, in which 100,000,000 random numbers were generated by our system and which resulted in returning the value of pi to 3.146564000, with a precision up to six decimal digits. The random numbers by GE-PRNG were compared against those generated by Python’s rand() function for sampling. The sampling results, when measured for accuracy against twenty-nine real world regression datasets, showed that GE-PRNG had less error when compared to Python’s rand() against the ground truths in seventeen of those, while there was no discernible difference in the remaining twelve.

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Section: Discussionmentioning

confidence: 99%

Design of a Cryptographically Secure Pseudo Random Number Generator with Grammatical Evolution

Ryan

Kshirsagar

Vaidya

et al. 2021

Preprint

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show abstract

“…Genetic algorithms are also a field of study, which has often been integrated with other disciplines, such as the encryption systems and pseudo-random generators to obtain better performance and security metrics. Kösemen et al (Kösemen et al 2018 ) present a pseudo-random generator based on genetic programming to be used in wireless identification platforms. Abdullah et al (Abdullah et al 2012 ) make use of the genetic algorithms in the process of image encryption process, via a chaotic function logistic map.…”

Section: Literature Surveymentioning

confidence: 99%

GPU-Accelerated implementation of a genetically optimized image encryption algorithm

et al. 2021

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This paper presents a GPU-accelerated implementation of an image encryption algorithm. The algorithm uses the concepts of a modified XOR cipher to encrypt and decrypt the images, with an encryption pad, generated using the shared secret key and some initialization vectors. It uses a genetically optimized pseudo-random generator that outputs a stream of random bytes of the specified length. The proposed algorithm is subjected to a number of theoretical, experimental, and mathematical analyses, to examine its performance and security against a number of possible attacks, using the following metrics - histogram analysis, correlation analysis, information entropy analysis, NPCR and UACI. The performance analysis carried out shows an average speedup-ratio of 3.489 for encryption, and 4.055 for decryption operation, between the serial and parallel implementations using GPU. The algorithm aims to provide better performance benchmarks, which can significantly improve the experience in the relevant use-cases, like real-time media applications.

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“…Kösemen at el. [24] developed a pseudorandom number generator by using genetic programming method. Genetic programming method uses Shannon entropy calculation for the fitness function.…”

Section: Related Workmentioning

confidence: 99%

XORSHIFTUL+: A Novel Hybrid Random Number Generator for Internet Of Things And Wireless Sensor Network Applications

Aydın¹,

Kösemen²

2020

Pamukkale J Eng Sci

Self Cite

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There are an increasing number of lightweight devices such as smart cards, radio frequency identification (RFID) tags, wireless sensor nodes and devices associated with the Internet of Things (IoT) concept, all of which need effective and lightweight security structures. One of the basic elements used in authentication protocols is the nonce values that are generated by PRNGs. Also random numbers are used for encryption process in secure communication. Proposed PRNG has demonstrated great suitability for devices with limited resources in terms of performance, resource usage and randomness. This generator is tested with NIST statistical test suite (NIST STS), which is one of the most comprehensive randomness test tools. It is also implemented, tested on wireless identification and sensing platform (WISP) passive RFID tag and compared with well-known PRNGs. As a result of the comparisons, it has better results than its rivals. Akıllı kartlar, radyo frekansı tanımlama (RFID) etiketleri, kablosuz sensör düğümleri ve Nesnelerin İnterneti (IoT) konsepti ile ilişkili düşük kaynaklı cihazların sayısında artış olmaktadır. Kimlik doğrulama protokollerinde kullanılan temel öğelerden biri SRSÜ'ler tarafından üretilen nonce değerleridir. Ayrıca güvenli iletişimde şifreleme işlemi için rasgele sayılar kullanılır. Önerilen SRSÜ, sınırlı kaynaklara sahip cihazlarda performans, kaynak kullanımı ve rastlantısallık açısından uygunluk göstermiştir. Bu üreteç en kapsamlı rastgele sayı test araçlarından biri olan NIST istatistiksel test takımı (NIST STS) ile test edilmiştir. Ayrıca, kablosuz tanıma ve algılama platformu (WISP) pasif RFID etiketi üzerinde test edilmiş ve uygulanmış ve iyi bilinen SRSÜ'lerle karşılaştırılmıştır. Karşılaştırmalar sonucunda rakiplerinden daha iyi sonuçlar vermiştir.

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Genetic programming-based pseudorandom number generator for wireless identification and sensing platform

Cited by 12 publications

References 25 publications

Design of a Cryptographically Secure Pseudo Random Number Generator with Grammatical Evolution

Design of a Cryptographically Secure Pseudo Random Number Generator with Grammatical Evolution

GPU-Accelerated implementation of a genetically optimized image encryption algorithm

XORSHIFTUL+: A Novel Hybrid Random Number Generator for Internet Of Things And Wireless Sensor Network Applications

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