Abstract
Cryptography is the art and science of protecting information form intruders of data by making the information unintelligible (encryption), as well as, to retrieve the original data (decryption). Good cryptography means that the information is encrypted in such a way that a brute force attack against the key or cryptography algorithm are all impossible. Up to date, several ciphers utilizing complex mathematics have been proposed. But none of them are entirely secure and their vulnerabilities have been exposed. Therefore, novel cryptography algorithms, capable of provide superior protection, are highly desirable. In proposed work, a method for generating a key from an alphanumeric login password is introduced and implementation of symmetric-key encryption and decryption using an autoencoder neural network. Our experiments show that proposed method overcome traditional cryptography algorithms, at lest when small text file are used, and it is extremely hard to crack.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ahmed, K., Rihan, S.D.: A performance comparison of encryption a performance comparison of encryption algorithms AES and DES. Int. J. Eng. Res. Technol. (IJERT) 4(December), 151–154 (2015)
Albawi, S., Mohammed, T.A., Al-Zawi, S.: Understanding of a convolutional neural network. In: Proceedings of 2017 International Conference on Engineering and Technology, ICET 2017, January–August 2018, pp. 1–6 (2018)
Basu, S., Karuppiah, M., Nasipuri, M., Halder, A.K., Radhakrishnan, N.: Bio-inspired cryptosystem with DNA cryptography and neural networks. J. Syst. Architect. 94, 24–31 (2019)
Bonnell, J.A.: Implementation of a New Sigmoid Function in Backpropagation Neural Networks (2011)
Caliskan, A., Yuksel, M.E.: Classification of coronary artery disease data sets by using a deep neural network. EuroBiotech J. 1(4), 271–277 (2017)
Shaza, R.D., Khalid, A., Osman, S.: A performance comparison of encryption a performance comparison of encryption algorithms AES and DES. Int. J. Eng. Res. Technol. (IJERT) 4(12), 151–154 (2015)
Hadke, P.P., Kale, S.G.: Use of neural networks in cryptography: a review. In: IEEE WCTFTR 2016 - Proceedings of 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare, pp. 1–4 (2016)
Fei, H., Wang, J., Xiaofei, X., Changjiu, P., Tao, P.: Batch image encryption using generated deep features based on stacked autoencoder network. Math. Probl. Eng. 2017, 256–261 (2017)
Jogdand, R.M., Bisalapur, S.S.: Design of an efficient neural key generation. Int. J. Artif. Intell. Appl. 2(1), 60–69 (2011)
Kalsi, S., Kaur, H., Chang, V.: DNA cryptography and deep learning using genetic algorithm with NW algorithm for key generation. J. Med. Syst. 42(1), 17 (2018)
Klimov, A., Mityagin, A., Shamir, A.: Analysis of neural cryptography. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2501, pp. 288–298 (2002)
Kumar, Y., Munjal, R., Sharma, H.: Comparison of symmetric and asymmetric cryptography with existing vulnerabilities and countermeasures. IJCSMS Int. J. Comput. Sci. Manag. Stud. 11(03), 2231–5268 (2011)
Li, S., Chen, G., Zheng, X.: Chaos-Based Encryption for Digital Images and Videos, December 2004
Maddodi, G., Awad, A., Awad, D., Awad, M., Lee, B.: A new image encryption algorithm based on heterogeneous chaotic neural network generator and DNA encoding. Multimed. Tools Appl. 77(19), 24701–24725 (2018)
Mahajan, P., Sachdeva, A.: A study of encryption algorithms AES, DES and RSA for security. Global J. Comput. Sci. Technol. Netw. Web Secur. 13(15), 64–69 (2013)
Mondal, M., Ray, K.: Review on DNA cryptography, pp. 1–31 (2019)
Ng, A.: Sparse autoencoder. CS294A Lect. Notes 72, 1–19 (2011)
Patil, P., Narayankar, P., Narayan, D.G., Meena, S.M.: A comprehensive evaluation of cryptographic algorithms: DES, 3DES, AES, RSA and blowfish. Proc. Comput. Sci. 78(December 2015), 617–624 (2016)
Popli, M., Popli, G.: DNA cryptography: a novel approach for data security using flower pollination algorithm. SSRN Electron. J. (2019)
Qian, Y., Dong, J., Wang, W., Tan, T.: Deep learning for steganalysis via convolutional neural networks. Med. Watermarking Secur. Forensics 9409(94090J), 2015 (2015)
Singh, A., Nandal, A.: Neural cryptography for secret key exchange and encryption with AES. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(5), 376–381 (2013)
Volna, E., Kotyrba, M., Kocian, V., Janosek, M.: Cryptography based on neural network. In: Proceedings - 26th European Conference on Modelling and Simulation, ECMS 2012 (2012). Accessed Feb 2015
Ye, J., Ni, J., Yi, Y.: Deep learning hierarchical representations for image steganalysis. IEEE Trans. Inf. Forensics Secur. 12(11), 2545–2557 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Quinga-Socasi, F., Zhinin-Vera, L., Chang, O. (2021). A Deep Learning Approach for Symmetric-Key Cryptography System. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1. FTC 2020. Advances in Intelligent Systems and Computing, vol 1288. Springer, Cham. https://doi.org/10.1007/978-3-030-63128-4_41
Download citation
DOI: https://doi.org/10.1007/978-3-030-63128-4_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63127-7
Online ISBN: 978-3-030-63128-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)