Abstract
In this paper it is shown how to find LFSR using genetic algorithm. LSFRs are part of many cryptographic structures and pseudorandom number generators. Applying genetic algorithms to Linear Feedback Shift Registers (LFSR) cryptanalysis is not quite obvious. Genetic algorithms – being one of heuristic techniques – give approximate solution. The solution could be very good, but not necessarily the best, whereas cryptographic problems require one exact answer, every other being not good enough. But as it will be shown, even if it is not intuitive, breaking LFSRs using genetic algorithms can give some interesting and promising results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Abd, A.A., Younis, H.A., Awad, W.S.: Attacking of stream Cipher Systems Using a Genetic Algorithm. Journal of the University of Thi Qar 6, 1–6 (2011)
Ahmad, A., Hayat, L.: Selection of Polynomials for Cyclic Redundancy Check for the use of High Speed Embedded – An Algorithmic Procedure. Wseas Transactions on Computers 10(1) (2011)
Bergmann, K.P.: Cryptanalysis Using Nature-Inspired Optimization Algorithms, master’s thesis (2007)
Garg, P., Shastri, A.: An Improved Cryptanalytic Attack on Knapsack Cipher using Genetic Algorithm. World Academy of Science, Engineering and Technology, 553–560 (2007)
Garg, P.: A Comparison between Memetic algorithm and Genetic algorithm for the cryptanalysis of Simplified Data Encryption Standard algorithm. International Journal of Network Security & Its Applications (IJNSA) 1(1), 34–42 (2009)
Goldberg, D.E.: Genetic algorithms in search, optimization, and machine learning, 3rd edn. Wydawnictwa Naukowo-Techniczne, Warszawa (2003) (in Polish)
Hołubowicz, W., Płóciennik, P.: GSM cyfrowy system telefonii komórkowej, 2nd edn. Przedsiębiorstwo Wielobranżowe “Rokon”, Poznań (1997)
Hospodar, G., et al.: Machine learning in side-channel analysis: a first study. J. Cryptogr. Eng., 293–302 (2011)
Mehrotra, A.: GSM System Engineering. Artech House, London (1997)
Robling-Denning, D.E.: Cryptography and Data Security. Wydawnictwa Naukowo-Techniczne, Warszawa (1995) (in Polish)
Russell, M.D., Clark, J.A., Stepney, S.: Making the Most of Two Heuristics: Breaking Transposition Ciphers with Ants (2003)
Schneier, B.: Applied Cryptography, 2nd edn. Wydawnictwa Naukowo-Techniczne, Warszawa (2002) (in Polish)
Selvi, G., Purusothaman, T.: Cryptanalysis of Simple Block Ciphers using Extensive Heuristic Attacks. European Journal of Scientific Research 78(2), 198–221 (2012)
Song, J., et al.: Cryptanalysis of Four-Round DES Based on Genetic Algorithm (2007)
Yaseen, I.: Breaking multiplicative knapsack ciphers using agenetic algorithm (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Polak, I., Boryczka, M. (2013). Breaking LFSR Using Genetic Algorithm. In: Bǎdicǎ, C., Nguyen, N.T., Brezovan, M. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2013. Lecture Notes in Computer Science(), vol 8083. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40495-5_73
Download citation
DOI: https://doi.org/10.1007/978-3-642-40495-5_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40494-8
Online ISBN: 978-3-642-40495-5
eBook Packages: Computer ScienceComputer Science (R0)