Abstract
Prediction of the next bit in pseudorandom sequences is one of the main issues in cryptology in order to prove the robustness of the systems. Linear complexity has served as a reference measurement to evaluate the randomness of the sequences, comparing them with the shortest LFSR that can generate those sequences. Several tools based on artificial intelligence have also been used for the next bit prediction, such as the C4.5 classifier. In this paper, we apply a different approach, the back propagation neural networks, to predict the sequences generated by LFSR. The results confirm that these networks can predict the entire sequence knowing less input patterns than techniques based on classifiers.
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.
References
Golomb, S.W.: Shift-Register Sequences, revised edn. Aegean Park Press, Laguna Hill (1982)
Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Prentice Hall (2008)
Hernández, J.C., Sierra, J.M., Mex-Perera, C., Borrajo, D., Ribagorda, A., Isasi, P.: Using the general next bit predictor like an evaluation criteria. In: Proc. of NESSIE Workshop, Leuven, Belgium (2000)
Hernández, J.C., Isasi, P., Sierra, J.M., Mex-Perera, C., Ramos, B.: Using classifiers to predict linear feedback shift registers. In: Proceedings IEEE 35th Annual 2001 International Carnahan Conference on Security Technology, pp. 240–249 (2001)
Khan, S.S.: Classificatory Prediction and Primitive Polynomial Construction of Linear Feedback Shift Registers using Decision Tree Approach. In: Fifth International Conference on Knowledge Based Computer Systems, KBCS 2004 (2004)
Kant, S., Khan, S.: Analyzing a class of pseudo-random bit generator through inductive machine learning paradigm. Intelligent Data Analysis 10, 539–554 (2006)
Kant, S., Kumar, N., Gupta, S., Singhal, A., Dhasmana, R.: Impact of machine learning algorithms on analysis of stream ciphers. In: Proceeding of International Conference on Methods and Models in Computer Science, ICM2CS 2009, pp. 251–258 (2009)
Massey, J.L.: Shift register synthesis and BCH decoding. IEEE Trans. Inform. Theory 15, 122–127 (1969)
Rueppel, R.: Stream Ciphers. In: Simmons, G.J. (ed.) Contemporary Cryptology, The Science of Information, pp. 65–134. IEEE Press (1992)
Ziv, J.: An efficient universal prediction algorithm for unknown sources with limited trained data. IEEE Trans. Inform. Theory 48, 1690–1693 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Peinado, A., Ortiz, A. (2014). Prediction of Sequences Generated by LFSR Using Back Propagation MLP. In: de la Puerta, J., et al. International Joint Conference SOCO’14-CISIS’14-ICEUTE’14. Advances in Intelligent Systems and Computing, vol 299. Springer, Cham. https://doi.org/10.1007/978-3-319-07995-0_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-07995-0_40
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07994-3
Online ISBN: 978-3-319-07995-0
eBook Packages: EngineeringEngineering (R0)