This research aims to demonstrate the functionalities and prediction capabilities of different neural networks on
substitution cipher. To acquire the best possible extent of applicability all possible plaintexts are verified. The normalized Feed
Forward Back Propagation Network with Feature Scaling approach results in 50% - 60% of accuracy whereas N – state Sequential
Machine with Jordan Network enhanced the accuracy to 100%. This comparative study illustrates the strength of recursive backpropagation
over simple back-propagation technique. The incompatibility to deal with plaintext with alphanumeric value makes NState
Sequential Machine less efficient. Further, in comparison with former researches on N-State Sequential Machine; that was
designed for 3-bit plaintext input with alphabet range [A-H], modifications are performed, to increases the accepted input bit range
from 3-bit to 5-bit. This increases the input alphabet acceptance range [A-Z] including few punctuations marks- but falls short to
deal with ‘alphanumeric values’. Finally, Chaotic Network is implemented and proved to be a most promising, efficient, significant
and well-suited technique to deal with all input plaintexts.
Published In:IJCSN Journal Volume 7, Issue 2
Date of Publication : April 2018
Pages : 49-53
Tables : 21
Anindita Das Bhattacharjee : Started her career in industry as a
trainee software developer for a year. She has done M. Tech in
Computer Science from National Institute of Technology (NIT),
Durgapur. She secured a position of First Class Second in M.
Tech. Currently she is working in Swami Vivekananda Institute of
Science and Technology. She has been teaching for about 10
years in Computer Science. She is an author of a book “Artificial
Intelligence and Soft Computing for Beginners” published in 2013
and author of book chapters in the book “Intelligent Analysis for
Multimedia Information” published by IGI Global and indexed in
Abhijit Mitra : Pursuing B. Tech. Final year student in Computer
Science and Engineering in Swami Vivekananda Institute of
Science and Technology.
Asiya Amreen Zaman : Pursuing B. Tech. Final year student in
Computer Science and Engineering in Swami Vivekananda
Institute of Science and Technology. Secured 3rd position in
International Olympiad of Science and 2nd position in Olympiad of
English 2011, New Delhi.
Chaotic Neural Network, Cryptography, Feature Scaling, Jordan Network, Modified Caesar Cipher, N – State
Sequential Machine, Pangrams, Unity Based Normalization
At the initial stage, it was intended to depict statistically
the prediction capability of three specific Neural
Networks on Modified Caesar Substitution Cipher
technique (with Substitution Factor k=6) to identify the
best suited Neural Networks. This research achieves
conclusion in interdependent five sequential stages.
 Prajapat, S., Thakur, R.S., “Various Approaches Towards
Cryptanalysis”, International Journal of Computer
Applications (0975 – 8887), Vol 127, No. 14, October 2015,
 Greydanus, S., “Learning the Enigma with Recurrent Neural
Networks”, Vol II, September 2017.
 Shukla, N., Tiwari, A., “An Empirical Investigation of
Using ANN Based N – State Sequential Machine and
Chaotic Neural Network in the Field of Cryptography”, Vol
XII, Issue X, Version I, 2012, pp. 17–26.
 Paritoshik, Choudhary, P., “Accomplishment of
Cryptography using Neural Network in Artificial
Intelligence”, International Journal of Advance Research,
Ideas and Innovations in Technology, pp. 144–55.
 Kamila, N.K., Rout, H., Dash, N., “Stego – Cryptography
Using Chaotic Neural Network”, American Journal of
Signal Processing, 2014, pp. 24–33.
 Gujral, V., Pradhan, S.K., “Cryptography Using Artificial
Neural Networks”, January, 2009.
 Assad, S.E., “Chaos Based Information Hiding and
Security”, In Proceeding: 7th International Conference for
Internet Technology and Secured Transactions, London, UK,
10 – 12 December 2012, pp. 67–72.
 Alallayah, K., Amin, M., AbdElwahed, W., Alhamami, A.,
“Applying Neural Networks for Simplified Data Encryption
Standard (SDES) Cipher System Cryptanalysis”, The
International Arab Journal of Information Technology Vol 9,
No. 2, March 2012, pp. 163–69.
 Raj, P., “Cryptography Using Neural Networks”, May, 2015.
 Kahate, A., (2013) “Cryptography and Network Security”,
3rd ed., New Delhi: McGraw Hill Education (India) Private
 Agarwal, B., Agarwal, H., “Survey Report on Chaos-
Based Cryptography”, International Journal of Applied
Science and Engineering Technology, No. 2, 2012, pp. 1–19.
 Yayik, A., Kutlu, Y., “Neural Network-Based
Cryptography”, pp. 177–92.
 Rathee, N., Sachdeva, R., Dalel, V., Jaie, Y., “A Novel
Approach for Cryptography Using Artificial Neural
Networks”, Vol 4, Issue 4, August 2016, pp. 187–193.
 “Domain 3: Cryptography”, Elsevier, October 2016.
 Zhang, B., Jin, C., “Cryptanalysis of a Chaos-based Stream
Cipher”, In Proceedings: 9th International Conference for
Young Computer Scientists, Hunan, China, 18 – 21
November 2008, pp. 2782–5.
 Baragad, S.R., Redd, P.S., “Studies on the Advancements
of Neural Network based Cryptalytic Works”, International
Journal of Emerging Trends and Technology in Computer
Science (IJETTCS), Vol 2, Issue 5, September – October,
2013, ISSN 2278-6856.
 Elkamshoushy, D.H., Aboulsoud, A.K., “Cryptographic
Schemes using Chaotic Systems”, In Proceedings: National
Radio Science Conference, Tanta, Egypt, 18 – 20 March
2008, pp. 1–6.
 Banerjee, S., Kurths, J., “Chaos and Cryptography: A new
dimension in secure communications”, The European
Physical Journal Vol XII, Issue X, Version I, 2012, pp.
 Laskari, E.C., Meletiou, G.C., Tasoulis, D.K., Vrahatis,
M.N., “Studying the performance of artificial neural
networks on problems related to cryptography”, Nonlinear
Analysis: Real World Applications, Vol 7, pp. 937 – 942,
 “Mathworks”, The Mathworks, Inc. Available from:
 Parker, T.S., Chua, L.O., “Chaos – A tutorial for
Engineers”, IEEE Proceedings, Vol 75, 1987, pp. 982–1008.
 John Justin, M., Manimurugan, S., “A survey on various
Encryption Techniques, International Journal of Soft
Computing and Engineering (IJSCE)”, ISSN: 2231 – 2307,
Vol 2, Issue 1, March 2012.
 Volna, E., Kotyrba, M., Kocien, V., Janosek, M.,
“Cryptography based on Neural Network”, Department of
Informatics and Computers University of Ostrava
Dvorakova, Czech Republic.
 El-Zoghabi, A.A., Yassin, A.H., Hussien, H.H. “Survey
Report on Cryptography Based on Neural Network”, Vol 3,
Issue 12, 2013, pp. 456–62.
 Lawande, Q.V., Ivan, B.R., Dhodapkar, S.D., “Chaos-
Based Cryptography: A New Approach to Secure
Communications”, No. 258, July 2005.
 Biederman, D.C., Ososanya, E., “Capacity of Several
Neural Networks with respect to Digital Adder and
 Yu, W., Cao, J., “Cryptography based on Delayed Chaotic
Neural Networks”, Department of Mathematics, Southeast
University, Nanjing, China.
 Klimoy, A., Mityagin, A., Shamir, A. “Analysis of Neural
Cryptography”, Advances in Cryptography – ASIACRYPT
2002, 8th International Conference on the Theory and
Application of Cryptography and Information Security, Queenstown, New Zealand, December 1 -5, 2002, pp. 288-
 Shobhy, M.I., Shehata, A.R., “Chaotic Algorithm for Data
Encryption”, In proceedings: IEEE International Conference
on Acoustics, Speech, and Signal, Salt Lake City, UT, USA,
07 – 11 May 2001, pp. 997–1000.
 Suryawanshi, S.B., Nawgaje, D.D., “Chaotic Neural
Network for Cryptography in Image Processing”, IJCA
Proceedings on 2nd National Conference on Information and
Communication Technology NCICT, November 2011.
 Agarwal, N., Agarwal, P., “Use of Artificial Neural
Network in the field of Security”, MIT International Journal
of Computer Science and Information Technology, Vol 3,
No 1, pp. 42 – 44, 2013.
 Su, S., Lin, A., Yen, J., “Design and Realization of a New
Chaotic Neural Encryption/Decryption Network”.
Conference on Recent Trends in Information Technology, 3-
5 June, 2012, pp. 107–12.
 Shukla, P.K., Khare, A., Rizvi, M.A, Stalin, S., Kumar, S.,
“Applied Cryptography Using Chaos Function for Fast
Digital Logic – based Systems in Ubiquitous Computing”,
2015, pp. 1387–1410.
 Alvarez, G., Montoya, F., Romera, M., Pastor, G.,
“Cryptanalysis of a Chaotic Encryption System”, 276, 2000,
 Kr?se, B., Smagt, van der, P., (1996) “An Introduction to
Neural Networks”, 8th ed., Amsterdam, Available from:
 Dalkiran, I., Danisman, K., “Artificial Neural Network
Based Chaotic Generator for Cryptology”, 2010, pp. 225–40.
 Kaur, H., Panag, T.S., “Cryptography using Chaotic
Neural Network”, International Journal of Information
Technology and Knowledge Management, Vol 4, No. 2,
July – December 2011, pp. 417–22.  Schmitz, R., “Use of chaotic dynamical systems in
cryptography”, J. Franklin Inst., 338, 2001, pp. 429-441.
 Alvarez, G., Li, S., “Some Basic Cryptographic
Requirements for Chaos – Based Cryptosystems”, Vol. 16,
2006, pp. 2129–51.
All rights reserverd @ IJCSN International Journal www.IJCSN.org