How to generate cryptographically strong sequences of pseudo-random bits
SIAM Journal on Computing
Inferring decision trees using the minimum description length principle
Information and Computation
Machine learning: paradigms and methods
Machine learning: paradigms and methods
Pseudorandom Bit Generators in Stream-Cipher Cryptography
Computer - Special issue on cryptography
C4.5: programs for machine learning
C4.5: programs for machine learning
Applied cryptography (2nd ed.): protocols, algorithms, and source code in C
Applied cryptography (2nd ed.): protocols, algorithms, and source code in C
Cryptography: Theory and Practice
Cryptography: Theory and Practice
Shift Register Sequences
Machine Learning
Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)
Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)
Inferring a sequence generated by a linear congruence
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Predicting subset sum pseudorandom generators
SAC'04 Proceedings of the 11th international conference on Selected Areas in Cryptography
A universal prediction lemma and applications to universal data compression and prediction
IEEE Transactions on Information Theory
A universal predictor based on pattern matching
IEEE Transactions on Information Theory
An efficient universal prediction algorithm for unknown sources with limited training data
IEEE Transactions on Information Theory
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Random number generation is an integral part of strong cipher systems. If a pseudo-random sequence can be predicted with better than chance probability then the generator is considered to be cryptographically weak. This paper deals with next bit prediction of pseudo-random binary sequences generated by Linear Feedback Shift Register (LFSR) and LFSR-based Pseudo-Random Bit Generators (PRBG), using inductive Machine Learning (ML) paradigm, namely C4.5 the most common and widely used inductive data mining algorithm. This machine learning technique has been introduced to convert the theoretical prediction problem into a classification problem, which we coined as Classificatory Prediction problem. We further extended the use of this technique to predict next bit without having any knowledge of subsequent bits of the PRBG and can be termed as true Next Bit Predictor. The technique used is independent of the parameters and domain knowledge of the pseudo-random bit generators. The present study is a comprehensive extension of the work done by Hernandez et al. [15]. We performed meticulous experiments (over wide range of LFSRs) and came out with a more explanatory analysis. Our classificatory prediction results paved the way for the evolution of the next bit prediction model.