Article 3321

Title of the article

Correlation test for code sequence randomness with almost linear computational complexity 

Authors

Vladimir I. Volchikhin, Doctor of engineering sciences, professor, president of Penza State University (40 Krasnaya street, Penza, Russia), E-mail: president@pnzgu.ru
Aleksandr I. Ivanov, Doctor of engineering sciences, associate professor, scientific adviser, Penza Research Institute of Electrical Engineering (9 Sovetskaya street, Penza, Russia), E-mail: ivan@pniei.penza.ru
Aleksey P. Ivanov, Candidate of engineering sciences, associate professor, head of the sub-department of technical facilities of information security, Penza State University (40 Krasnaya street, Penza, Russia), E-mail: ap_ivanov@pnzgu.ru
Aleksey V. Strokov, Deputy head of the office of access and information security, Organizational & Technological Solutions 2000 LLC (60A Dmitrovskoye highway, Moscow, Russia), E-mail: strokov.alexey@otr.ru 

Index UDK

004.056:519.24 

DOI

10.21685/2072-3059-2021-3-3

Abstract

Background. The purpose of this article is to create and study a new test of random code sequence quality with low computational complexity. Materials and methods. It is proposed, by analogy with the classical calculation of the autocorrelation function of noise with continual samples, to use the autocorrelation function for discrete noise. It is proposed to receive almost “white” noise from a software pseudo-random number generator. Samples of “colored” noise are proposed to be obtained by a sliding convolution of eight adjacent readings of “white” noise without weighing them. Results. The sum of the modules of the first 7 samples of the autocorrelation function of the analyzed noise is a powerful criterion for testing the hypothesis of independence of discrete data with a sample of 256 bits. This criterion has a low almost linear computational complexity and at the same time gives a high level of linear separability of dependent and independent data. The power of this new statistical test is higher than the power of similar statistical tests based on calculating Hamming distances. Conclusions. The proposed statistical criterion can be used when testing biometric authentication codes in a small-sized trusted computing environment with low bit depth, low power consumption, and a small amount of long-term and random access memory. 

Key words

evaluation of white noise quality, statistical criteria for testing the hypothesis of independence, low computational complexity 

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References

1. Strokov A.V., Kazantsev E.I. A software tool for generating truly random cryptographic keys from an ambiguous biometric component of a user's handwriting dynamics. Bezopasnost' in-formatsionnykh tekhnologiy: trudy I Vseros. nauch.-tekhn. konf. = Information technology security: proceedings of the 1st All-Russian scientific and engineering conference. Penza: Izd-vo PGU, 2019:139–143. (In Russ.)
2. Yunin A.P., Ivanov A.I., Strokov A.V., Makhsudov C.P. Neural network generalization of three standard tests of quality control of “white noise” obtained by hashing a random part of biometric data. Bezopasnost' informatsionnykh tekhnologiy: sb. nauch. st. po materialam II Vseros. nauch.-tekhn. konf. = Information technology security: proceedings of the 2nd All-Russian scientific and engineering conference. Penza: Izd-vo PGU, 2020:49–56. (In Russ.)
3. Kriptograficheskaya khesh-funktsiya = Cryptographic hash function. (In Russ.). Available at: https://ru.wikipedia.org/wiki/Kriptografi-cheskaya_khesh-funktsiya (accessed 02.04.2021).
4. Tsiklicheskiy izbytochnyy kod = Cyclic redundancy code. (In Russ.). Available at: https://ru.wikipedia.org/wiki/Tsiklicheskiy izbytochnyy_kod (accessed 02.04.2021).
5. Grigor'ev A.Yu. Testing methods for generators of random and pseudo-random sequences. Uchenye zapiski UlGU. Seriya: Matematika i informatsionnye tekhnologii = Proceedings of Ulyanovsk State University. Series: Mathematics and informational technologies. 2017;(1):22–28. (In Russ.)
6. Volchikhin V.I., Ivanov A.I., Yunin A.P., Malygina E.A. Multidimensional portrait of digital sequences of ideal “white noise” in Hamming convolutions. Izvestiya vysshikh uchebnykh zavedeniy. Povolzhskiy region. Tekhnicheskie nauki = University proceedings. Volga region. Engineering sciences. 2017;(4):4–13. (In Russ.). doi:10.21685/ 2072-3059-2017-4-1
7. Yunin A.P., Ivanov A.I., Ratnikov K.A., Kol'chugina E.A. The quality assessment of “white noise”: implementation of the “flock of monkeys” test through a set of Hamming convolutions built for different number systems. Izvestiya vysshikh uchebnykh zavedeniy. Povolzhskiy region. Tekhnicheskie nauki = University proceedings. Volga region. Engineering sciences. 2018;(4):54–64. (In Russ.). doi:10.21685/2072-3059-2018-4-5
8. Ivanov A.I., Yunin A.P. Embrion iskusstvennogo intellekta: kompaktnaya neyrosetevaya proverka kachestva sluchaynykh posledovatel'nostey, poluchennykh iz biometricheskikh dannykh: preprint = Artificial intelligence embryo: compact neural network quality check of random sequences obtained from biometric data: preprint. Penza: Izd-vo PGU, 2021:68. (In Russ.)
9. Ivanov A.I., Kubasov I.A., Samokutyaev A.M. Testing large neural networks on small samples. Nadezhnost' i kachestvo slozhnykh system = Reliability and quality of complex systems. 2020;(3):72–79. (In Russ.). doi:10.21685/2307-4205-2020-3-9
10. Ivanov A.I. Iskusstvennyy intellekt vysokogo doveriya. Uskorenie vychisleniy i ekonomiya pamyati pri testirovanii bol'shikh setey iskusstvennykh neyronov na malykh vyborkakh. Sistemy bezopasnosti = Security systems. 2020;(5):60–62. (In Russ.)

 

Дата создания: 09.12.2021 08:46
Дата обновления: 09.12.2021 09:26