Impact of machine learning algorithms on analysis of stream ciphers

Stream ciphers are widely used for information security. The keystream produced by a cipher must be unpredictable. Attacks on stream ciphers typically exploit some underlying patterns existing in the keystream. The objective of this paper is to develop such an attack with the help of machine learning algorithms. The Linear Feedback Shift Register (LFSR) has been solved for several test cases using machine learning algorithms. We also study some variants of LFSR and Geffe Generator and propose a model for predicting the future bits of a keystream generator. The results for Geffe Generator using this model have been presented. However the approach did not yield encouraging results when confronted with the keystream generators of the eSTREAM project.