Revolutionizing Cryptography: A Cutting-Edge Substitution Box Design Through Trigonometric Transformation
Keywords:
Substitution-Box, Linear Trigonometric Transformation, Security, Cryptosystems.Abstract
This paper proposes an innovative approach to enhance the robustness of substitution boxes in cryptography by employing chaotic mapping. Our methodology leverages chaotic mapping to construct a robust 8 × 8 S-box that adheres to the requirements of a bijective function. An illustrative example of such an S-box is presented, accompanied by a comprehensive analysis employing established metrics such as nonlinearity, bijection, bit independence, strict avalanche effect, linear approximation probability, and differential uniformity. To evaluate its strength, we benchmark the performance of our proposed S-box against recently investigated counterparts. Our findings reveal that our approach to S-box construction is both pioneering and efficacious in fortifying substitution boxes for cryptography. Given the escalating frequency of cyber threats and hacking incidents, safeguarding online communication and personal information has become increasingly challenging. Cryptography plays a pivotal role in addressing these challenges by transforming data into a more secure format. In this research, we introduce a novel, lightweight algorithm grounded in trigonometric principles, which significantly enhances security and reduces susceptibility to hacking attempts. Comparative evaluations demonstrate the superior performance of our algorithm over established methods such as the Hill cipher, Blowfish, and DES. While conventional approaches prioritize security, they often incur delays due to increased computational load. Our objective is to expedite cryptographic processes without compromising security, achieved through the strategic application of trigonometric principles. Our algorithm capitalizes on trigonometric functions and operations to introduce confusion, thereby thwarting hacking attempts. Extensive research and testing substantiate that our algorithm excels in both security and speed compared to traditional methods. By seamlessly integrating trigonometric concepts into a streamlined design, our algorithm proves to be practical for real-world applications, offering a robust solution for safeguarding data on the Internet.
References
S. Xu, Y. Wang, Y. Guo, and C. Wang, “A novel chaos-based image encryption scheme,” Proc. - 2009 Int. Conf. Inf. Eng. Comput. Sci. ICIECS 2009, 2009, doi: 10.1109/ICIECS.2009.5365275.
S. Bukhari, A. Yousaf, S. Niazi, and M. R. Anjum, “A Novel Technique for the Generation and Application of Substitution Boxes (s-box) for the Image Encryption,” Nucl., vol. 55, no. 4, pp. 219–225, 2018, Accessed: Dec. 09, 2023. [Online]. Available: http://thenucleuspak.org.pk/index.php/Nucleus/article/view/229
M. Ge and R. Ye, “A novel image encryption scheme based on 3D bit matrix and chaotic map with Markov properties,” Egypt. Informatics J., vol. 20, no. 1, pp. 45–54, Mar. 2019, doi: 10.1016/J.EIJ.2018.10.001.
I. Hussain, A. Anees, T. A. Al-Maadeed, and M. T. Mustafa, “Construction of S-Box Based on Chaotic Map and Algebraic Structures,” Symmetry 2019, Vol. 11, Page 351, vol. 11, no. 3, p. 351, Mar. 2019, doi: 10.3390/SYM11030351.
W. Yan and Q. Ding, “A Novel S-Box Dynamic Design Based on Nonlinear-Transform of 1D Chaotic Maps,” Electron. 2021, Vol. 10, Page 1313, vol. 10, no. 11, p. 1313, May 2021, doi: 10.3390/ELECTRONICS10111313.
S. Shaukat Jamal, D. Shah, A. Deajim, and T. Shah, “The Effect of the Primitive Irreducible Polynomial on the Quality of Cryptographic Properties of Block Ciphers,” Secur. Commun. Networks, vol. 2020, 2020, doi: 10.1155/2020/8883884.
X. Zhang et al., “Generation and Evaluation of a New Time-Dependent Dynamic S-Box Algorithm for AES Block Cipher Cryptosystems,” IOP Conf. Ser. Mater. Sci. Eng., vol. 978, no. 1, p. 012042, Nov. 2020, doi: 10.1088/1757-899X/978/1/012042.
V. Kumar and A. Girdhar, “A 2D logistic map and Lorenz-Rossler chaotic system based RGB image encryption approach,” Multimed. Tools Appl., vol. 80, no. 3, pp. 3749–3773, Jan. 2021, doi: 10.1007/S11042-020-09854-X/METRICS.
X. yuan Wang and Q. Yu, “A block encryption algorithm based on dynamic sequences of multiple chaotic systems,” Commun. Nonlinear Sci. Numer. Simul., vol. 14, no. 2, pp. 574–581, Feb. 2009, doi: 10.1016/J.CNSNS.2007.10.011.
S. Hanis and R. Amutha, “A fast double-keyed authenticated image encryption scheme using an improved chaotic map and a butterfly-like structure,” Nonlinear Dyn., vol. 95, no. 1, pp. 421–432, Jan. 2019, doi: 10.1007/S11071-018-4573-7/METRICS.
H. Gao, Y. Zhang, S. Liang, and D. Li, “A new chaotic algorithm for image encryption,” Chaos, Solitons & Fractals, vol. 29, no. 2, pp. 393–399, Jul. 2006, doi: 10.1016/J.CHAOS.2005.08.110.
X. Wang and D. Chen, “A parallel encryption algorithm based on piecewise linear chaotic map,” Math. Probl. Eng., vol. 2013, 2013, doi: 10.1155/2013/537934.
N. A. Khan, M. Altaf, and F. A. Khan, “Selective encryption of JPEG images with chaotic based novel S-box,” Multimed. Tools Appl., vol. 80, no. 6, pp. 9639–9656, Mar. 2021, doi: 10.1007/S11042-020-10110-5/METRICS.
L. C. Nizam Chew and E. S. Ismail, “S-box Construction Based on Linear Fractional Transformation and Permutation Function,” Symmetry 2020, Vol. 12, Page 826, vol. 12, no. 5, p. 826, May 2020, doi: 10.3390/SYM12050826.
K. Ishaq and A. A. Qarni, “An Innovative Design of Substitution Box Using Trigonometric Transformation,” Aug. 2023, Accessed: Dec. 26, 2023. [Online]. Available: https://arxiv.org/abs/2311.16107v1
H. Liu, B. Zhao, and L. Huang, “Quantum Image Encryption Scheme Using Arnold Transform and S-box Scrambling,” Entropy 2019, Vol. 21, Page 343, vol. 21, no. 4, p. 343, Mar. 2019, doi: 10.3390/E21040343.
K. Z. Zamli, “Optimizing S-box generation based on the Adaptive Agent Heroes and Cowards Algorithm,” Expert Syst. Appl., vol. 182, p. 115305, Nov. 2021, doi: 10.1016/J.ESWA.2021.115305.
X. Wang et al., “S-Box Based Image Encryption Application Using a Chaotic System without Equilibrium,” Appl. Sci. 2019, Vol. 9, Page 781, vol. 9, no. 4, p. 781, Feb. 2019, doi: 10.3390/APP9040781.
X. Wang, J. Yang, and N. Guan, “High-sensitivity image encryption algorithm with random cross diffusion based on dynamically random coupled map lattice model,” Chaos, Solitons & Fractals, vol. 143, p. 110582, Feb. 2021, doi: 10.1016/J.CHAOS.2020.110582.
M. Ahmad, E. Al-Solami, A. M. Alghamdi, and M. A. Yousaf, “Bijective S-Boxes Method Using Improved Chaotic Map-Based Heuristic Search and Algebraic Group Structures,” IEEE Access, vol. 8, pp. 110397–110411, 2020, doi: 10.1109/ACCESS.2020.3001868.
H. Alsaif, R. Guesmi, A. Kalghoum, B. M. Alshammari, and T. Guesmi, “A Novel Strong S-Box Design Using Quantum Crossover and Chaotic Boolean Functions for Symmetric Cryptosystems,” Symmetry 2023, Vol. 15, Page 833, vol. 15, no. 4, p. 833, Mar. 2023, doi: 10.3390/SYM15040833.
Z. Jiang and Q. Ding, “Construction of an S-Box Based on Chaotic and Bent Functions,” Symmetry 2021, Vol. 13, Page 671, vol. 13, no. 4, p. 671, Apr. 2021, doi: 10.3390/SYM13040671.
M. F. Khan, K. Saleem, M. A. Alshara, and S. Bashir, “Multilevel information fusion for cryptographic substitution box construction based on inevitable random noise in medical imaging,” Sci. Reports 2021 111, vol. 11, no. 1, pp. 1–23, Jul. 2021, doi: 10.1038/s41598-021-93344-z.
Q. Lu, C. Zhu, and X. Deng, “An Efficient Image Encryption Scheme Based on the LSS Chaotic Map and Single S-Box,” IEEE Access, vol. 8, pp. 25664–25678, 2020, doi: 10.1109/ACCESS.2020.2970806.
X. Liu, X. Tong, Z. Wang, and M. Zhang, “Efficient high nonlinearity S-box generating algorithm based on third-order nonlinear digital filter,” Chaos, Solitons & Fractals, vol. 150, p. 111109, Sep. 2021, doi: 10.1016/J.CHAOS.2021.111109.
M. Ahmad, U. Shamsi, and I. R. Khan, “An Enhanced Image Encryption Algorithm Using Fractional Chaotic Systems,” Procedia Comput. Sci., vol. 57, pp. 852–859, Jan. 2015, doi: 10.1016/J.PROCS.2015.07.494.
“Image Encryption based on Chaotic Map and Reversible Integer Wavelet Transform.” Accessed: Dec. 09, 2023. [Online]. Available: https://sciendo.com/article/10.2478/jee-2014-0013
A. Razzaque et al., “An efficient S-box design scheme for image encryption based on the combination of a coset graph and a matrix transformer,” Electron. Res. Arch. 2023 52708, vol. 31, no. 5, pp. 2708–2732, 2023, doi: 10.3934/ERA.2023137.
L. Liu, Y. Zhang, and X. Wang, “A Novel Method for Constructing the S-Box Based on Spatiotemporal Chaotic Dynamics,” Appl. Sci. 2018, Vol. 8, Page 2650, vol. 8, no. 12, p. 2650, Dec. 2018, doi: 10.3390/APP8122650.
F. ul Islam and G. Liu, “Designing S-Box Based on 4D-4Wing Hyperchaotic System,” 3D Res., vol. 8, no. 1, pp. 1–9, Mar. 2017, doi: 10.1007/S13319-017-0119-X/METRICS.
M. A. Arshed, S. Mumtaz, O. Riaz, W. Sharif, and S. Abdullah, “A Deep Learning Framework for Multi-Drug Side Effects Prediction with Drug Chemical Substructure,” Int. J. Innov. Sci. Technol., vol. 4, no. 1, pp. 19–31, 2022.
M. T. Ubaid, M. Z. Khan, M. Rumaan, M. A. Arshed, M. U. G. Khan, and A. Darboe, “COVID-19 SOP’s Violations Detection in Terms of Face Mask Using Deep Learning,” 4th Int. Conf. Innov. Comput. ICIC 2021, 2021, doi: 10.1109/ICIC53490.2021.9692999.
M. A. Arshed, H. Ghassan, M. Hussain, M. Hassan, A. Kanwal, and R. Fayyaz, “A Light Weight Deep Learning Model for Real World Plant Identification,” 2022 2nd Int. Conf. Distrib. Comput. High Perform. Comput. DCHPC 2022, pp. 40–45, 2022, doi: 10.1109/DCHPC55044.2022.9731841.
H. Younis, M. A. Arshed, F. ul Hassan, M. Khurshid, and H. Ghassan, “Tomato Disease Classification using Fine-Tuned Convolutional Neural Network,” Int. J. Innov. Sci. Technol., vol. 4, no. 1, pp. 123–134, 2022, doi: 10.33411/ijist/2022040109.
M. Mubeen, M. A. Arshed, and H. A. Rehman, “DeepFireNet - A Light-Weight Neural Network for Fire-Smoke Detection,” Commun. Comput. Inf. Sci., vol. 1616 CCIS, pp. 171–181, 2022, doi: 10.1007/978-3-031-10525-8_14/COVER.
M. A. Arshed, S. Mumtaz, M. Hussain, R. Alamdar, M. T. Hassan, and M. Tanveer, “DeepFinancial Model for Exchange Rate Impacts Prediction of Political and Financial Statements,” 3rd IEEE Int. Conf. Artif. Intell. ICAI 2023, pp. 13–19, 2023, doi: 10.1109/ICAI58407.2023.10136658.
R. A. A. Shahzad, M. A. Arshed, F. Liaquat, M. Tanveer, M. Hussain, “Pneumonia Classification from Chest X-ray Images Using Pre-Trained Network Architectures,” VAWKUM trans. Comput. sci., vol. 10, no. 2, pp. 34–44, 2022.
M. T. M. A. Arshed, A. Shahzad, K. Arshad, D. Karim, S. Mumtaz, “Multiclass Brain Tumor Classification from MRI Images using Pre-Trained CNN Model,” VFAST trans. softw. eng., vol. 10, no. 4, pp. 22–28, 2022.
M. A. Arshed et al., “Machine Learning with Data Balancing Technique for IoT Attack and Anomalies Detection,” Int. J. Innov. Sci. Technol., vol. 4, no. 2, pp. 490–498, 2022, doi: 10.33411/ijist/2022040218.
H. A. Arshad, M. Hussain, A. Amin, and M. A. Arshed, “Impact of Artificial Intelligence in COVID-19 Pandemic: A Comprehensive Review,” 2022 2nd Int. Conf. Distrib. Comput. High Perform. Comput. DCHPC 2022, pp. 66–73, 2022, doi: 10.1109/DCHPC55044.2022.9732091.
M. A. Arshed, W. Qureshi, M. Rumaan, M. T. Ubaid, A. Qudoos, and M. U. G. Khan, “Comparison of Machine Learning Classifiers for Breast Cancer Diagnosis,” 4th Int. Conf. Innov. Comput. ICIC 2021, 2021, doi: 10.1109/ICIC53490.2021.9692926.
M. A. Arshed and F. Riaz, “Machine Learning for High Risk Cardiovascular Patient Identification 1,” J. Distrib. Comput. Syst., vol. 4, no. 2, pp. 34–39, 2021, Accessed: Dec. 26, 2023. [Online]. Available: https://ijdcs.ir/wp-content/uploads/2022/08/IJDCS-8-6.pdf
M. Hussain, A. Shahzad, F. Liaquat, M. A. Arshed, S. Mansoor, and Z. Akram, “Performance Analysis of Machine Learning Algorithms for Early Prognosis of Cardiac Vascular Disease,” Tech. J., vol. 28, no. 02, pp. 31–41, Jun. 2023, Accessed: Dec. 26, 2023. [Online]. Available: https://tj.uettaxila.edu.pk/index.php/technical-journal/article/view/1778
M. F. Idris, J. Sen Teh, J. L. S. Yan, and W. Z. Yeoh, “A Deep Learning Approach for Active S-Box Prediction of Lightweight Generalized Feistel Block Ciphers,” IEEE Access, vol. 9, pp. 104205–104216, 2021, doi: 10.1109/ACCESS.2021.3099802.
G. Kim, H. Kim, Y. Heo, Y. Jeon, and J. Kim, “Generating Cryptographic S-Boxes Using the Reinforcement Learning,” IEEE Access, vol. 9, pp. 83092–83104, 2021, doi: 10.1109/ACCESS.2021.3085861.
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