LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Tahar, Mekhaznia (2015)
Publisher: Advanced Technology and Science (ATScience)
Journal: International Journal of Intelligent Systems and Applications in Engineering
Languages: English
Types: Article
Subjects: Cryptanalysis, Feistel ciphers, bat algorithm
Recent cryptosystems constitute an effective task for cryptanalysis algorithms due to their internal structure based on nonlinearity. This problem can be formulated as NP-Hard. It has long been subject to various attacks; available results, emerged many years ago remain insufficient when handling large instances due to resources requirement which increase with the amount of processed data.  On another side, optimization techniques inspired by swarm intelligence represents a set of approaches used to solve complex problems. This is mainly due to their fast convergence with a consumption of reduced resources. The purpose of this paper is to provide, and for a first time, a more detailed study about the performance of BAT algorithm in cryptanalysis of some variant of Data encryption standard algorithms. Experiments were performed to study the effectiveness of the used algorithm in solving the considered problem and underline the difficulties encountered.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] S. Rao & al. (2009). Cryptanalysis of a Feistal Type Block Cipher by Feed Forward Neural Network Using Right Sigmoidal Signals. International Journal of Software Computing, Vol.4(3).
    • [2] S.Ali K, Al-Omari Putra Sumari. (2010). Spiking Neurons with ASNN BASED-Methods for the Neural Block Cipher. International journal of computer science & information Technology. Vol.2(4).
    • [3] R. Singh, D. B. Ojha. (2010). An Ordeal Random Data Encryption Scheme (ORDES). International Journal of Engineering Science and Technology. Vol. 2(11). Pages.6349- 6360.
    • [4] C. Blum, X. Li, (2007). Swarm intelligence in optimization', natural Computing Series, Springer.
    • [5] T.S.C. Felix, M.K. Tiwari. (2007). Swarm Intelligence, Focus on Ant Particle Swarm Optimization. Int. Tech Education and Publishing..978-902613-09-7.Austria.
    • [6] A. Gherboudj, S. Chikhi. (2011). A modified HPSO Algorithms for Knapsack Problem. CCIS. Springer.
    • [7] G.S. Sharvani, N.K. Cauvery, T.M. Rangaswamy. (2009). Different Types of Swarm Intelligence Algorithm for Routing. International Conference on Advances in Recent Technologies in Communication and Computing.
    • [8] Beni, G., Wang, J. (1989). Swarm Intelligence in Cellular Robotic Systems, Proceed. NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy.
    • [9] J. Olamaei, T. Niknam, G. Gharehpetian. (2008). Application of particle swarm optimization for distribution feeder reconfiguration considering distributed generators. AMC. Pages 575-586.
    • [10] X. S. Yang. (2010). A New Metaheuristic Bat-Inspired Algorithm. Nature Inspired Cooperative Strategies for Optimization.
    • [11] D. R. Griffin. (1958). Listening in the dark. Yale Univ. Press. New York.
    • [12] J. R,. Speakman, P. A. Racey. (1991). The cost of being a bat. Nature. V 350. Pages. 421−423
    • [13] S. Ghorui & al. (2000). A simpler and elegant algorithm for computing fractal dimension in higher dimensional state space. PRAMANA Journal of Physics. Indian Academy of Sciences. Vol 54(2), L331-L336.
    • [14] W. Stallings. (2008). Cryptography and Network Security Principles and Practices. Pearson Education.
    • [15] A. B. Forouzan. (2008). Cryptography and Network Security. Tata McGraw hill Education, 2nd ed.
    • [16] E.C. Laskari & al. (2005). Evolutionary computation based cryptanalysis: A first study. Nonlinear Analysis: Theory, Methods and Applications. Vol. 63. Pages. e823-e830.
    • [17] E. Schaefer. (1996). A Simplified Data Encryption Standard Algorithm. Cryptologia. Vol. 20(1). Pages. 77-84.
    • [18] Robert, L. (2000). Cryptological Mathematics. The Mathematical Association of America. NY.
    • [19] Nelson, G., Wallis, G. and Bas, A. (2000). Exploring Natural Languag: Working with the British Component of the International Corpus of English. John Benjamins Publishing Company. Amsterdam.
    • [20] Singh, S. (1999). The code book: The Evolution of Secrecy from Mary, Queen of Scots, to Quantum Cryptography. Doubleday. New York, NY, USA, 1st edition.
    • [21] Beker, H. and Piper, F. (1982). Cipher Systems: The Protection of Communications. John Wiley & Sons.
    • [22] Jakobsen, T. and Knudsen, L.R. (2001). Attacks on block ciphers of low algebraic degree. J. of Cryptology. Vol. 14(3). Pages. 197-210.
    • [23] Nalini, N. and Raghavendra, G. (2006). Cryptanalysis of Simplified Data Encryption Standard via Optimisation Heuristics. Int. J. of Computer Science and Network Security. Vol. 6(1B). Pages.240-246.
    • [24] Verma, A. K., Dave, M. and Joshi. R. C. (2007). Genetic Algorithm and Tabu Search Attack on the MonoAlphabetic Subsitution Cipher in Adhoc Networks. Journal of Computer Science. Vol. 3 (3). Pages. 134-137.
    • [25] Ganapathi, S. and Purusothaman, T. (2011). Reduction of Key Search Space of Vigenere Cipher Using Particle Swarm Optimization. J. of Computer Science. Vol 7(11). Pages. 1633-1638.
  • No related research data.
  • No similar publications.