Analyzing a class of pseudo-random bit generator through inductive machine learning paradigm

Abstract

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.