Modelling Cryptographic Distinguishers Using Machine Learning
Journal article, 2022

Cryptanalysis is the development and study of attacks against cryptographic primitives and protocols. Many cryptographic properties rely on the difficulty of generating an adversary who, given an object sampled from one of two classes, correctly distinguishes the class used to generate that object. In the case of cipher suite distinguishing problem, the classes are two different cryptographic primitives. In this paper, we propose a methodology based on machine learning to automatically generate classifiers that can be used by an adversary to solve any distinguishing problem. We discuss the assumptions, a basic approach for improving the advantage of the adversary as well as a phenomenon that we call the “blind spot paradox”. We apply our methodology to generate distinguishers for the NIST (DRBG) cipher suite problem. Finally, we provide empirical evidence that the distinguishers might statistically have some advantage to distinguish between the DRBG used.

Cipher Suite Distinguishing Problem

Machine Learning

Cryptanalysis

Distinguisher

Pseudo Random Generator

Author

Carlo Brunetta

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Pablo Picazo-Sanchez

Chalmers, Computer Science and Engineering (Chalmers), Information Security

Journal of Cryptographic Engineering

2190-8508 (ISSN) 2190-8516 (eISSN)

Vol. 12 2 123-135

Areas of Advance

Information and Communication Technology

Subject Categories

Computational Mathematics

Other Mathematics

Information Science

DOI

10.1007/s13389-021-00262-x

More information

Latest update

7/4/2022 1