Prediction of Sequences Generated by LFSR Using Back Propagation MLP
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.
KeywordsCryptography LFSR pseudorandom sequences neural networks bit prediction
Unable to display preview. Download preview PDF.
- 1.Golomb, S.W.: Shift-Register Sequences, revised edn. Aegean Park Press, Laguna Hill (1982)Google Scholar
- 2.Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Prentice Hall (2008)Google Scholar
- 3.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)Google Scholar
- 4.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)Google Scholar
- 5.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)Google Scholar
- 6.Kant, S., Khan, S.: Analyzing a class of pseudo-random bit generator through inductive machine learning paradigm. Intelligent Data Analysis 10, 539–554 (2006)Google Scholar
- 7.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)Google Scholar
- 9.Rueppel, R.: Stream Ciphers. In: Simmons, G.J. (ed.) Contemporary Cryptology, The Science of Information, pp. 65–134. IEEE Press (1992)Google Scholar