Abstract
Nowadays, the research in deoxyribonucleic acid (DNA) cryptography seeks to implement data transmission techniques to ensure secure data transmission across the world. As data transmission techniques are not secured due to the presence of hackers and attackers, a DNA-based cryptosystem can be suitable to secure data transmission, where confidential information (plaintext) is encoded in an unreadable form (ciphertext) prior to its transmission. This paper proposes a novel cryptosystem based on DNA cryptography and finite state machines. Here, finite state machines perform substitution operations on the DNA sequence and make the system more secure. Moreover, a DNA character conversion table is proposed in this paper to increase the randomness of the ciphertext. The efficiency of the proposed scheme is tested in terms of the randomness of the ciphertext. The randomness of the ciphertext determines the security of a cryptosystem, and here, randomness tests mentioned in the National Institute of Standards and Technology (NIST) test suite assess the randomness of the ciphertext. The experimental results show that the proposed scheme yields an average P-value of 0.95, which outperforms the existing systems. The proposed scheme guarantees a highly secured cryptosystem as an average avalanche effect of 75.65% is achieved. As a result, the proposed scheme is more secure than the existing DNA-based cryptosystems.
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References
Kumar, S., Wollinger, T.: Fundamentals of symmetric cryptography. In: Lemke, K., Paar, C., Wolf, M. (eds.) Embedded Security in Cars, pp. 125–143. Springer, Cham (2006)
Abbas, S., et al.: Improving security of the Internet of Things via RF fingerprinting based device identification system. Neural Comput. Appl. 33, 14753–14769 (2021). https://doi.org/10.1007/s00521-021-06115-2
Namasudra, S., et al.: Securing multimedia by using DNA based encryption in the cloud computing environment. ACM Trans. Multimed. Comput. Commun. Appl. 16(3s), 1–19 (2020)
Namasudra, S.: Fast and secure data accessing by using DNA computing for the cloud environment. IEEE Trans. Serv. Comput. (2020). https://doi.org/10.1109/TSC.2020.304647
Li, Q., et al.: A novel grayscale image steganography scheme based on chaos encryption and generative adversarial networks. IEEE Access 8, 168166–168176 (2020). https://doi.org/10.1109/ACCESS.2020.3021103
Singh, R., Rajpal, N., Mehta, R.: An empiric analysis of wavelet-based feature extraction on deep learning and machine learning algorithms for arrhythmia classification. Int. J. Interact. Multimed. Artif. Intell. 6(6), 25–34 (2021)
Hema, S., Basavegowda, Guesh, D.: Deep learning approach for microarray cancer data classification. CAAI Trans. Intell. Technol. 5(1), 22–33 (2020)
Zhou, W., et al.: A comprehensive review on deep learning approaches in wind forecasting applications. CAAI Trans. Intell. Technol. (2021). https://doi.org/10.1049/cit2.12076
Hossain, E.M.S., et al.: A DNA cryptographic technique based on dynamic DNA sequence table. In: Proceedings of the IEEE International Conference on Computer and Information Technology (ICCIT), pp. 270–275. IEEE, Bangladesh (2016)
Niu, Y., et al.: Review on DNA cryptography. In: Proceedings of the International Conference on Bio-Inspired Computing: Theories and Applications, pp. 134–148. Springer, Cham (2019)
Karatas, M., Karacan, I., Tozan, H.: An integrated multi-criteria decision making methodology for health technology assessment. Eur. J. Indust. Eng. 12(4), 504–534 (2018)
Karacan, I., Tozan, H., Karatas, M.: Multi criteria decision methods in health technology assessment: a brief literature review. Eurasian J. Health Technol. Assess. 1(1), 12–19 (2016)
Khan, A.N., et al.: A study of incremental cryptography for security schemes in mobile cloud computing environments. In: Proceedings of the IEEE Symposium on Wireless Technology & Applications (ISWTA), pp. 62–67. (2013). https://doi.org/10.1109/ISWTA.2013.6688818
Wang, T., “EIHDP, et al.: Edge-intelligent hierarchical dynamic pricing based on cloud-edge-client collaboration for IoT systems. IEEE Trans. Comput. 70(8), 1285–1298 (2021). https://doi.org/10.1109/TC.2021.3060484
Dogan, K., Karatas, M., Yakici, E.: A model for locating preventive health care facilities. Cent. Eur. J. Oper. Res. 28(3), 1091–1121 (2020)
Rashid, A., et al.: RC-AAM: blockchain-enabled decentralized role-centric authentication and access management for distributed organizations. Cluster Comput. 24, 3551–3571 (2021). https://doi.org/10.1007/s10586-021-03352-x
Namasudra, S., et al.: Towards DNA based data security in the cloud computing environment. Comput. Commun. 151, 539–547 (2020)
Nayak, P., Nayak, S.K., Das, S.: A secure and efficient color image encryption scheme based on two chaotic systems and advanced encryption standard. In: Proceedings of International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, pp. 412–418. (2018)
Khan, A.N., et al.: A cloud-manager-based re-encryption scheme for mobile users in cloud environment: a hybrid approach. J. Grid Comput. 13(4), 651–675 (2015). https://doi.org/10.1007/s10723-015-9352-9
Sun, Y., et al.: Automatically designing CNN architectures using the genetic algorithm for image classification. IEEE Trans. Cybern. 50(9), 3840–3854 (2020). https://doi.org/10.1109/TCYB.2020.2983860
Majumdar, A., et al.: A novel DNA-inspired encryption strategy for concealing cloud storage. Front. Comput. Sci. (2021). https://doi.org/10.1007/s11704-019-9015-2
Biswas, M.R., et al.: A technique for DNA cryptography based on dynamic mechanisms. J. Inform. Secur. Appl. 48, 3840–3854 (2019). https://doi.org/10.1016/j.jisa.2019.102363
Imdad, M., Ramli, S.N., Mahdin, H.: Increasing randomization of ciphertext in DNA cryptography. Int. J. Adv. Comput. Sci. Appl. (2021). https://doi.org/10.14569/IJACSA.2021.0121047
Malathi, P., et al.: Highly improved DNA based steganography. Procedia Comput. Sci. 115, 651–659 (2017). https://doi.org/10.1016/j.procs.2017.09.151
Gao, J., et al.: Decentralized federated learning framework for the neighborhood: a case study on residential building load forecasting. In: Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems, pp. 453–459. ACM, Portugal (2021). https://doi.org/10.1145/3485730.3493450
Raut, H.T., et al.: Enhanced bat algorithm for COVID-19 short-term forecasting using optimized LSTM. Soft. Comput. 25(20), 12989–12999 (2021). https://doi.org/10.1007/s00500-021-06075-8
Ali, H.M., et al.: Planning a secure and reliable IoT-enabled FOG-assisted computing infrastructure for healthcare. Cluster Comput. (2021). https://doi.org/10.1007/s10586-021-03389-y
Biswas, M.R., et al.: A DNA cryptographic technique based on dynamic DNA encoding and asymmetric cryptosystem. In: Proceedings of the 4th International Conference on Networking, Systems and Security, IEEE, pp. 1–8. (2017)
Ibrahim1, F.E., Abdalkader, H.M., Moussa, M.I.: Enhancing the security of data hiding using double DNA sequences. In: Proceedings of the Industry-Academia Collaboration Conference (IAC), Cairo, pp. 6–8. (2015)
Zhang, S., Gao, T.: A novel data hiding scheme based on DNA coding and module-N operation. Int. J. Multimed. Ubiquitous Eng. 10(4), 337–344 (2015)
Gupta, R., Singh, R.: An improved substitution method for data encryption using DNA sequence and CDMB. In: Proceedings of the 3rd International Symposium, India, pp. 197–206. (2015)
Wang, Z., Yu, Z.: Index- based symmetric DNA encryption algorithm. In: Proceedings of the 4th International Congress on Image and Signal Processing, pp. 2290–2294. Shanghai, IEEE (2011)
Kalyani, S., Gulati, N.: Pseudo DNA cryptography technique using OTP key for secure data transfer. Int. J. Eng. Sci. Comput. 6(5), 5657–5663 (2016)
Disabato, S., Roveri, M., Alippi, C.: Distributed deep convolutional neural networks for the internet-of-things. IEEE Trans. Comput. 70(8), 1239–1252 (2021)
Ejiyi, C.J., et al.: Comparative analysis of building insurance prediction using some machine learning algorithms. Int. J. Interact. Multimed. Artif. Intell. 7(3), 75–85 (2022)
Wang, Y., et al.: Hiding messages based on DNA sequence and recombinant DNA technique. IEEE Trans. Nanotechnol. 18, 299–307 (2019)
Reddy, M.I., Kumar, A.P.S., Reddy, K.S.: A secured cryptographic system based on DNA and a hybrid key generation approach. Biosystems (2020). https://doi.org/10.1016/j.biosystems.2020.104207
Jin, X., et al.: Deep learning-based side channel attack on HMAC SM3. Int. J. Interact. Multimed. Artif. Intell. 6(4), 113–120 (2020)
Enayatifar, R., Guimarães, F.G., Siarry, P.: Index-based permutation diffusion in multiple-image encryption using DNA sequence. Opt. Lasers Eng. 115, 131–140 (2019). https://doi.org/10.1016/j.optlaseng.2018.11.01
Priya, S., Saritha, S.: A robust technique to generate unique code DNA sequence. In: Proceedings of the IEEE International Conference. on Energy, Communication, Data analytics and Soft computing (ICECDS), pp. 3815–3820. (2017). https://doi.org/10.1109/ICECDS.2017.8390178
Mohammad, K.H., Doreswamy, H.: Network anomaly detection using deep learning techniques. CAAI Trans. Intell. Technol. (2022). https://doi.org/10.1049/cit2.12078
Pujari, S.K., Bhattacharjee, G., Bhoi, S.: A hybridized model for image encryption through genetic algorithm and DNA sequence. Procedia Comput. Sci. 125, 165–171 (2018). https://doi.org/10.1016/j.procs.2017.12.023
Pavithran, P., et al.: A novel cryptosystem based on DNA cryptography, hyperchaotic systems and a randomly generated Moore machine for cyber physical systems. Comput. Commun. 188, 1–12 (2022). https://doi.org/10.1016/j.comcom.2022.02.008
Garapati, P., Musala, S.: Moore and mealy negative edge detector a VHDL example for finite state machine. In: Proceedings of the International Conference on Communication and Signal Processing (ICCSP), pp. 1159–1161 (2020). https://doi.org/10.1109/ICCSP48568.2020.9182310
Pavithran, P., Mathew, S., Namasudra, S., Lorenz, P.: A novel cryptosystem based on DNA cryptography and randomly generated Mealy machine. Comput. Secur. (2021). https://doi.org/10.1016/j.cose.2020.102160
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PP is the main author of this paper, who has conceived the idea and discussed it with all co-authors. SM has developed the main algorithms. SN is the corresponding author, and has performed the experiments of this paper. AS has supervised the entire work, evaluated the performance and proofread the paper.
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Pavithran, P., Mathew, S., Namasudra, S. et al. Enhancing randomness of the ciphertext generated by DNA-based cryptosystem and finite state machine. Cluster Comput 26, 1035–1051 (2023). https://doi.org/10.1007/s10586-022-03653-9
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DOI: https://doi.org/10.1007/s10586-022-03653-9