Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
DE
EN
Books
Journals
Events
Topic Page
Marketing
Start single access now
Access for companies
12th International Engine Congress 2025 | 25. + 26. February 2025

Meeting Place for the Powertrain and Sustainable Fuels Community

The congress will examine the future role of the internal combustion engine and its non-fossil fuels from a global perspective with leading experts. Be part of it! More information and booking options are available on our website.
EXTENDED SEARCH
Log in
Top
Annals of Data Science
Published in:

18-01-2022

A Review on Applications of Chaotic Maps in Pseudo-Random Number Generators and Encryption

Authors: Rasika B. Naik, Udayprakash Singh

Published in: Annals of Data Science | Issue 1/2024

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Because of the COVID-19 pandemic, most of the tasks have shifted to an online platform. Sectors such as e-commerce, sensitive multi-media transfer, online banking have skyrocketed. Because of this, there is an urgent need to develop highly secure algorithms which can not be hacked into by unauthorized users. The method which is the backbone for building encryption algorithms is the pseudo-random number generator based on chaotic maps. Chaotic maps are mathematical functions that generate a highly arbitrary pattern based on the initial seed value. This manuscript gives a summary of how the chaotic maps are used to generate pseudo-random numbers and perform multimedia encryption. After carefully analyzing all the recent literature, we found that the lowest correlation coefficient was 0.00006, which was achieved by Ikeda chaotic map. The highest entropy was 7.999995 bits per byte using the quantum chaotic map. The lowest execution time observed was 0.23 seconds with the Zaslavsky chaotic map and the highest data rate was 15.367 Mbits per second using a hyperchaotic map. Chaotic map-based pseudo-random number generation can be utilized in multi-media encryption, video-game animations, digital marketing, chaotic system simulation, chaotic missile systems, and other applications.
previous article Enhancing Diabetic Retinopathy Diagnosis with ResNet-50-Based Transfer Learning: A Promising Approach
next article Progressive Type-II Censored Samples for Bivariate Weibull Distribution with Economic and Medical Applications

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Chen L (2017) Cryptography standards in quantum time: new wine in old wineskin? IEEE Secur Privacy 15(4):51CrossRef
2.
Panagiotou P, Sklavos N, Darra E, Zaharakis ID (2020) Cryptographic system for data applications, in the context of internet of things. Microprocess Microsyst 72:102921CrossRef
3.
Özkaynak F (2014) Cryptographically secure random number generator with chaotic additional input. Nonlinear Dyn 78(3):2015–2020MathSciNetCrossRef
4.
Luo W, Takeuchi N, Chen O, Yoshikawa N (2021) Low-autocorrelation random number generator based on adiabatic quantum-flux-parametron logic. IEEE Trans Appl Supercond 31(5):1–5CrossRef
5.
Montfort N, Bogost I (2009) Random and raster: display technologies and the development of videogames. IEEE Annals History Comput 31(3):34–43MathSciNetCrossRef
6.
Duarte AEL (2020) Algorithmic interactive music generation in videogames. SoundEffects-Interdiscipl J Sound Sound Exp 9(1):38–59CrossRef
7.
Schindler W, Killmann W (2002) Evaluation criteria for true (physical) random number generators used in cryptographic applications. Int Workshop Cryptograph Hardw Embed Syst. Springer, Berlin, Heidelberg, pp 431–449
8.
Lim D, Ranasinghe DC, Devadas S, Jamali B, Abbott D, and Cole PH (2005) Exploiting metastability and thermal noise to build a reconfigurable hardware random number generator. In: Noise in Devices and Circuits III, vol. 5844. International Society for Optics and Photonics, pp. 294–309
9.
Ahmed HE-dH, Kalash HM, and Allah OSF (2007) An efficient chaos-based feedback stream cipher (ecbfsc) for image encryption and decryption. Informatica, vol. 31, no. 1
10.
Morrison F (2012) The art of modeling dynamic systems: forecasting for chaos, randomness and determinism. Courier Corporation
11.
Kitagawa G (1987) Non-gaussian state-space modeling of nonstationary time series. J Am Stat Assoc 82(400):1032–1041MathSciNet
12.
Staroswiecki M, Comtet-Varga G (2001) Analytical redundancy relations for fault detection and isolation in algebraic dynamic systems. Automatica 37(5):687–699MathSciNetCrossRef
13.
Thau F (1973) Observing the state of non-linear dynamic systems. Int J control 17(3):471–479CrossRef
14.
Soliman RF, Amin M, Abd El-Samie FE (2019) A modified cancelable biometrics scheme using random projection. Annals Data Sci 6(2):223–236CrossRef
15.
Majeed A (2019) Improving time complexity and accuracy of the machine learning algorithms through selection of highly weighted top k features from complex datasets. Annals Data Sci 6(4):599–621CrossRef
16.
Shi Y, Tian Y, Kou G, Peng Y, Li J (2011) Optimization based data mining. Springer, ChamCrossRef
17.
Olson DL, Shi Y, Shi Y (2007) Introduction to business data mining, vol 10. McGraw-Hill, New York
18.
Li C, Lin D, Feng B, Lü J, Hao F (2018) Cryptanalysis of a chaotic image encryption algorithm based on information entropy. IEEE Access 6:834–842CrossRef
19.
Tien JM (2017) Internet of things, real-time decision making, and artificial intelligence. Annals Data Sci 4(2):149–178CrossRef
20.
Stoyanov B, Ivanova T (2019) Chaosa: chaotic map based random number generator on Arduino platform. AIP Conf Proc 2172(1):090001CrossRef
21.
Kordov K and Stoyanov B (2017) Least significant bit steganography using Hitzl-Zele chaotic map. Int J Electr Telecommun 63
22.
Suryadi M, Ramli K et al (2017) On the design of Henon and logistic map-based random number generator. J Phys: Conf Series 893(1):012060
23.
Stoyanov B, Kordov K (2015) Novel secure pseudo-random number generation scheme based on two tinkerbell maps. Adv Studies Theor Phys 9(9):411–421CrossRef
24.
25.
Moysis L, Tutueva A, Christos K, Butusov D (2020) A chaos based pseudo-random bit generator using multiple digits comparison. Chaos Theory Appl 2(2):58–68
26.
Akhshani A, Akhavan A, Mobaraki A, Lim S-C, Hassan Z (2014) Pseudo random number generator based on quantum chaotic map. Commun Nonlinear Sci Numer Simul 19(1):101–111ADSCrossRef
27.
Alawida M, Samsudin A, Teh JS et al (2019) Digital cosine chaotic map for cryptographic applications. IEEE Access 7:609–622CrossRef
28.
Saber M and Eid MM (2021) Low power pseudo-random number generator based on lemniscate chaotic map. Int J Electr Comput Eng (2088-8708), vol. 11, no. 1
29.
Dridi F, El Assad S, Youssef WEH, Machhout M, Samhat AE (2021) Design, FPGA-based implementation and performance of a pseudo random number generator of chaotic sequences. Adv Electrical Comput Eng 21(2):41–48CrossRef
30.
Hamza R, Titouna F (2016) A novel sensitive image encryption algorithm based on the Zaslavsky chaotic map. Inf Secur J: Global Perspect 25(4–6):162–179
31.
Hua Z, Zhou Y, Huang H (2019) Cosine-transform-based chaotic system for image encryption. Inf Sci 480:403–419CrossRef
32.
Çiçek S (2020) Microcontroller-based random number generator implementation by using discrete chaotic maps. Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi 24(5):832–844CrossRef
33.
Krishnamoorthi S, Jayapaul P, Dhanaraj RK, Rajasekar V, Balusamy B, Islam SH (2021) Design of pseudo-random number generator from turbulence padded chaotic map. Nonlinear Dyn 104(2):1627–1643CrossRef
34.
Pan H, Lei Y, Jian C (2018) Research on digital image encryption algorithm based on double logistic chaotic map. EURASIP J Image Video Process 2018(1):1–10CrossRef
35.
Avaroğlu E (2017) Pseudorandom number generator based on Arnold cat map and statistical analysis. Turkish J Electr Eng Comput Sci 25(1):633–643CrossRef
36.
37.
Nejati H, Beirami A, Ali WH (2012) Discrete-time chaotic-map truly random number generators: design, implementation, and variability analysis of the zigzag map. Analog Integr Circuits Signal Process 73(1):363–374CrossRef
38.
Von Neumann J (1963) Various techniques used in connection with random digits. John von Neumann, Collect Works 5:768–770
39.
Cang S, Kang Z, Wang Z (2021) Pseudo-random number generator based on a generalized conservative Sprott-A system. Nonlinear Dyn 104(1):827–844CrossRef
40.
Lv J, Li X, Yang T, Yu H, Liu B (2022) A general pseudo-random number generator based on chaos. 4th EAI International conference on robotic sensor networks. Springer, Cham, pp 103–109CrossRef
41.
Yang L, Xiao-Jun T (2012) A new pseudorandom number generator based on a complex number chaotic equation. Chin Phys B 21(9):090506CrossRef
42.
Yu F, Li L, He B, Liu L, Qian S, Zhang Z, Shen H, Cai S, Li Y (2021) Pseudorandom number generator based on a 5D hyperchaotic four-wing memristive system and its FPGA implementation. Eur Phys J Spl Topics 230:1763–1772ADSCrossRef
43.
Nannipieri P, Di Matteo S, Baldanzi L, Crocetti L, Belli J, Fanucci L, Saponara S (2021) True random number generator based on Fibonacci-Galois ring oscillators for FPGA. Appl Sci 11(8):3330CrossRef
44.
Demir K, Ergun S (2019) Random number generators based on irregular sampling and Fibonacci-Galois ring oscillators. IEEE Trans Circuits Syst II: Expr Briefs 66(10):1718–1722
45.
Wu H, Yin Z, Xie J, Ding P, Liu P, Song H, Chen X, Xu S, Liu W, Zhang Y (2021) Design and implementation of true random number generators based on semiconductor superlattice chaos. Microelectron J 114:105119CrossRef
46.
Hua Z, Zhou Y, Pun C-M, Chen CP (2015) 2d sine logistic modulation map for image encryption. Inf Sci 297:80–94CrossRef
47.
Chen G, Mao Y, Chui CK (2004) A symmetric image encryption scheme based on 3D chaotic cat maps. Chaos, Solitons Fractals 21(3):749–761ADSMathSciNetCrossRef
48.
Gupta MD, Chauhan R (2021) Secure image encryption scheme using 4D-hyperchaotic systems based reconfigurable pseudo-random number generator and s-box. Integration 81:137–159CrossRef
49.
50.
Deng Z, Zhong S (2019) A digital image encryption algorithm based on chaotic mapping. J Algorithms Comput Technol 13:1748302619853470MathSciNetCrossRef
51.
Kari AP, Navin AH, Bidgoli AM, Mirnia M (2021) A new image encryption scheme based on hybrid chaotic maps. Multimed Tools Appl 80(2):2753–2772CrossRef
52.
53.
Alemami Y, Mohamed MA, Atiewi S, Mamat M (2020) Speech encryption by multiple chaotic maps with fast fourier transform. Int J Electr Comput Eng 10:5658–5664
54.
Shah D, Shah T, Ahamad I, Haider MI, Khalid I (2021) A three-dimensional chaotic map and their applications to digital audio security. Multimed Tools Appl 80(14):251–273CrossRef
55.
Shah D, Shah T, Jamal SS (2020) Digital audio signals encryption by mobius transformation and hénon map. Multimed Syst 26(2):235–245
56.
Hua Z, Zhou B, Zhou Y (2018) Sine chaotification model for enhancing chaos and its hardware implementation. IEEE Trans Ind Electron 66(2):1273–1284CrossRef
57.
Hu H, Liu L, Ding N (2013) Pseudorandom sequence generator based on the chen chaotic system. Comput Phys Commun 184(3):765–768ADSMathSciNetCrossRef
58.
59.
Liu L, Miao S, Hu H, Deng Y (2016) Pseudorandom bit generator based on non-stationary logistic maps. IET Inf Secur 10(2):87–94CrossRef
60.
Belazi A, Abd El-Latif AA, Belghith S (2016) A novel image encryption scheme based on substitution-permutation network and chaos. Signal Process 128:155–170CrossRef
61.
Wang X-Y, Zhang J-J, Zhang F-C, Cao G-H (2019) New chaotical image encryption algorithm based on fisher-yatess scrambling and DNA coding. Chin Phys B 28(4):040504ADSCrossRef
62.
Norouzi B, Seyedzadeh SM, Mirzakuchaki S, Mosavi MR (2014) A novel image encryption based on hash function with only two-round diffusion process. Multimed Syst 20(1):45–64CrossRef
63.
Akhshani A, Behnia S, Akhavan A, Hassan HA, Hassan Z (2010) A novel scheme for image encryption based on 2d piecewise chaotic maps. Opt Commun 283(17):3259–3266ADSCrossRef
64.
Akhavan A, Samsudin A, Akhshani A (2011) A symmetric image encryption scheme based on combination of nonlinear chaotic maps. J Frank Inst 348(8):1797–1813MathSciNetCrossRef
65.
Huang C-K, Liao C-W, Hsu S, Jeng Y (2013) Implementation of gray image encryption with pixel shuffling and gray-level encryption by single chaotic system. Telecommun Syst 52(2):563–571
66.
Wu Y, Noonan JP, Yang G, Jin H (2012) Image encryption using the two-dimensional logistic chaotic map. J Electron Imag 21(1):013014ADSCrossRef
67.
Wang X-Y, Zhang Y-Q, Bao X-M (2015) A novel chaotic image encryption scheme using DNA sequence operations. Opt Laser Eng 73:53–61ADSCrossRef
68.
Xu L, Li Z, Li J, Hua W (2016) A novel bit-level image encryption algorithm based on chaotic maps. Opt Lasers Eng 78:17–25CrossRef
69.
Norouzi B, Seyedzadeh SM, Mirzakuchaki S, Mosavi MR (2015) A novel image encryption based on row-column, masking and main diffusion processes with hyper chaos. Multimed Tools Appl 74(3):781–811CrossRef
70.
Karakaya B, Celik V, and Gulten A (2018) Realization of delayed cellular neural network model on FPGA. In: 2018 Electric electronics, computer science, biomedical engineerings meeting (EBBT)
Metadata
Title
A Review on Applications of Chaotic Maps in Pseudo-Random Number Generators and Encryption
Authors
Rasika B. Naik
Udayprakash Singh
Publication date
18-01-2022
Publisher
Springer Berlin Heidelberg
Published in
Annals of Data Science / Issue 1/2024
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-021-00364-7

Other articles of this Issue 1/2024

A Novel G Family for Single Acceptance Sampling Plan with Application in Quality and Risk Decisions

Modeling the Impact of Delay on the Aggregation of AD Proteins

Machine Learning Algorithms for Crime Prediction under Indian Penal Code

Context-Based Bigram Model for POS Tagging in Hindi: A Heuristic Approach

Progressive Type-II Censored Samples for Bivariate Weibull Distribution with Economic and Medical Applications

A Comprehensive Study of Artificial Intelligence and Cybersecurity on Bitcoin, Crypto Currency and Banking System

Premium Partner

    Image Credits