Skip to main content

Advertisement

SpringerLink
Log in

DNA Cryptography and Deep Learning using Genetic Algorithm with NW algorithm for Key Generation

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Cryptography is not only a science of applying complex mathematics and logic to design strong methods to hide data called as encryption, but also to retrieve the original data back, called decryption. The purpose of cryptography is to transmit a message between a sender and receiver such that an eavesdropper is unable to comprehend it. To accomplish this, not only we need a strong algorithm, but a strong key and a strong concept for encryption and decryption process. We have introduced a concept of DNA Deep Learning Cryptography which is defined as a technique of concealing data in terms of DNA sequence and deep learning. In the cryptographic technique, each alphabet of a letter is converted into a different combination of the four bases, namely; Adenine (A), Cytosine (C), Guanine (G) and Thymine (T), which make up the human deoxyribonucleic acid (DNA). Actual implementations with the DNA don’t exceed laboratory level and are expensive. To bring DNA computing on a digital level, easy and effective algorithms are proposed in this paper. In proposed work we have introduced firstly, a method and its implementation for key generation based on the theory of natural selection using Genetic Algorithm with Needleman-Wunsch (NW) algorithm and Secondly, a method for implementation of encryption and decryption based on DNA computing using biological operations Transcription, Translation, DNA Sequencing and Deep Learning.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Adleman, L., Molecular computation of solutions to combinatorial problems. Sci. 266:1021–1024, 1994.

    Article  CAS  Google Scholar 

  2. Stallings, W., Network security essentials, Prentice Hall, Fourth edition, 2011

  3. Delman, B.,Genetic Algorithms in Cryptography, MS Thesis 2004.

  4. Mislovaty, R., Klein, E., Kanter, I. and Kinzel, W. Security of neural cryptography, Proceedings of the 2004 11th IEEE International Conference on Electronics, Circuits and Systems, 2004. ICECS 2004, 2004, pp. 219–221.

  5. Anurag Roy and Asoke Nath, “DNA Encryption Algorithms: Scope and Challenges in Symmetric Key Cryptography”, IJIRAE 2016

  6. John H Reif, Michael Hauser, Michael Pirrung and Thomas LaBean, “Application of Biomolecular Computing to Medical Science: A Biomolecular Database System forStorage, Processing & Retrieval of Genetic Information & Material”, Duke University, 2006

  7. Junling Sun, “Sequence Splicing Techniques and Their Applications For Information Encryption”, International Conference on Advanced Mechatronic Systems, Tokyo, Japan, September I S-21, 2012

  8. V. M. M. Shyam, N. Kiran, “A novel encryption scheme based on DNA computing,” In 14th IEEE International Conference, Tia, India, Dec. 2007

  9. Yunpeng Zhang and Liu He Bochen Fu, “Research on DNA Cryptography”, College of Software and Microelectronics, Northwestern Polytechnical University, Xi’an,China

  10. Risca, V.I., DNA-based steganography. Cryptologia, Tylor and Francis. 25(1):37–49, 2001.

    Article  Google Scholar 

  11. Kaur H, Ahmed J, Scaria V, Computational analysis and In-silico predictive modeling for inhibitors of PhoP regulon in S. typhi on high-throughput screening bioassay dataset., Interdisciplinary Sciences: Computational Life Sciences (a Springer SCI Journal),2016.

  12. Kaur, H., Chauhan, R., Wasan, S. K. A Bayesian network model for probability estimation, Encyclopaedia of Information Science and Technology, IGI Global, Third Edition, 1551–1558, 2015.

  13. Kaur, H., Chauhan, R., and Ahmed, Z., Role of data mining in establishing strategic policies for the efficient management of healthcare system–a case study from Washington DC area using retrospective discharge data. BMC Health Services Research. 12(S1):P12, 2012.

    Article  PubMed Central  Google Scholar 

  14. Chauhan, R., Kaur, R. Predictive Analytics and Data Mining: A Framework for Optimizing Decisions with R Tool, Advances in Secure Computing, Internet Services, and Applications, Springer, 73–88, 2014.

  15. Kaur, H., Chauhan, R., and Alam, M.A., Spatial Clustering Algorithm using R-tree. Journal of Computing. 3(2):85–90, 2011.

    Google Scholar 

  16. Hermans, M. and Schrauwen, B. Training and analysing deep recurrent neural networks. In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 26, pages 190–198. Curran Associates, Inc., 2013.

  17. Chen, C., Xiang, H., Qiu, T., Wang, C., Zhou, Yang., Chang, V. A rear-end collision prediction scheme based on deep learning in the Internet of Vehicles, Journal of Parallel and Distributed Computing, 2017.

  18. Liao, X., Yin, J., Guo, S., Li, X., & Sangaiah, A. K. (2017). Medical JPEG image steganography based on preserving interblock dependencies. Computers & Electrical Engineering.

  19. Zheng, H. T., Wang, Z., Ma, N., Chen, J., Xiao, X., & Sangaiah, A. K. (2017). Weaklysupervised image captioning based on rich contextual information. Multimedia Tools and Applications, 1–17.

  20. Zhang, R., Shen, J., Wei, F., Li, X., & Sangaiah, A. K. (2017). Medical image classification based on multi-scale non-negative sparse coding. Artificial Intelligence in Medicine.

  21. Diffie, W., and Hellman, M., New directions in cryptography. IEEE Transaction on Information Theory. 22(6):644–654, 1976.

    Article  Google Scholar 

  22. EI Gamal T., A public-key cryptosystem and a signature scheme based on discrete logarithms. IEEE Transactions on Information Theory. 31(4):469–472, 1985.

    Article  Google Scholar 

  23. Borda M. & Tornea O. DNA secret writing techniques [C]. In COMM(2010), Chengdu: IEEE, June 10-12, 2010: 451–456

  24. Hongjun Liu, Xingyuan Wang and Abdurahman Kadir, “Image encryption using DNA complementary rule and chaotic maps”, ScienceDirect, 2012

  25. Martin JAVUREK and Marcel HARAKAĽ, “Cryptography And Genetic Algorithms”, Science & Military, 2016

  26. Tornea, O., and Borda, M.E., DNA Cryptographic Algorithms, MEDITECH 2009. IFMBE Proceedings. 26:223–226, 2009.

    Article  Google Scholar 

  27. U.Noorul Hussain, T. Chithralekha and A.Naveen Raj, G.Sathish, A.Dharani, “A Hybrid DNA Algorithm for DES using Central Dogma of Molecular Biology (CDMB)”, International Journal of Computer Applications, 2012

  28. K. Li, S. Zou, and J. Xv, Fast parallel molecular algorithms for DNAbased computation: Solving the elliptic curve discrete logarithm problem over gf(2 n), Journal of Biomedicine and Biotechnology, Hindawi., vol. 2008, pp. 1–10, Apr. 2008

  29. Fastest DNA Computer. Science, 2005, 308: 195

  30. Roweis, S., Winfreel, E., Burgoyne, R., et al., A sticker based model for DNA computation. Journal of Computational Biology. 5(4):615–629, 1998.

    Article  CAS  PubMed  Google Scholar 

  31. Tornea, O., and Borda, M.E., DNA Cryptographic Algorithms. IFMBE Proceedings. 26:223–226, 2009.

    Article  Google Scholar 

  32. Goyat, S.: Cryptography Using Genetic Algorithms (GAs). In: IOSR Journal of Computer Engineering (IOSRJCE), 1(5), pp. 06- 08 Identification of Common Molecular Subsequences. J. Mol. Biol. 147, 195-197. 2012.

  33. Mishra, S., and Bali, S., Public key cryptography using genetic algorithm. Int. J. Recent Technology and Engineering. 2(2):150–154, 2013.

    CAS  Google Scholar 

  34. A. J. Bagnall, “The Applications of Genetic Algorithms in Cryptanalysis”, School of Information Systems, University Of East Anglia, 1996.

  35. Kaur, H., & Tao, X. (Eds.). ICTs and the millennium development goals: A United Nations perspective. New York, Springer, US, 2014.

  36. Kaur H, Lechman E and Marszk A (Eds.), Catalyzing Development through ICT Adoption: The Developing World Experience, Springer Publishers, Switzerland, 2017.

  37. A. Tragha, F. Omary, A. Kriouile, “Genetic Algorithms Inspired Cryptography”, A.M.S.E Association for the Advancement of Modeling & Simulation Techniques in Enterprises, Series D: Computer Science and Statistics, November 2007.

  38. A.J. Umbarkar and P.D. Sheth, “Crossover Operators In Genetic Algorithms: A Review”, Ictact Journal On Soft Computing, October 2015

  39. Watson J D, Hopkins N H, Roberts J W, et al. Molecular Biology of the Gene. 4th ed. Menlo Park, CA: The Benjamin/Cummings Publishing Co., Inc., 1987

  40. Taylor, C., Risca, V., and Bancroft, C., Hiding messages in DNA microdots. Nature. 399:533–534, 1999.

    Article  Google Scholar 

  41. Needleman, S.B., and Wunsch, C.D., A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48:443–453, 1970.

    Article  CAS  PubMed  Google Scholar 

  42. Qin Limin. The Study of DNA - Based Encryption Method [D].Zheng Zhou: Zheng Zhou University of Light Industry, 2008.

  43. Zhi-min Zhou and Zhong-wen Chen, “Dynamic Programming for Protein Sequence Alignment”, International Journal of Bio-Science and Bio-Technology Vol. 5, No. 2, April, 2013

Download references

Acknowledgements

This research work is catalyzed and supported by National Council for Science and Technology Communications (NCSTC), Department of Science and Technology (DST), Ministry of Science and Technology (Govt. of India) for support and motivation [grant recipient: Dr. Harleen Kaur]. The authors gratefully acknowledge financial support from the Ministry of Science and Technology (Govt. of India), India.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi, India

    Shruti Kalsi & Harleen Kaur

  2. Xerox Technology Ltd., 5th-6th Floor, Vatika Business Park, Gurugram, Haryana, India

    Shruti Kalsi

  3. International Business School Suzhou, Xi’an Jiaotong Liverpool University, Suzhou, China

    Victor Chang

Authors
  1. Shruti Kalsi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Harleen Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Victor Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Harleen Kaur.

Ethics declarations

Conflict of Interest

None.

Additional information

This article is part of the Topical Collection on Image & Signal Processing

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kalsi, S., Kaur, H. & Chang, V. DNA Cryptography and Deep Learning using Genetic Algorithm with NW algorithm for Key Generation. J Med Syst 42, 17 (2018). https://doi.org/10.1007/s10916-017-0851-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-017-0851-z

Keywords

Search

Navigation