Modelling Cryptographic Distinguishers Using Machine Learning
Artikel i vetenskaplig tidskrift, 2021

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.

Pseudo Random Generator

Cryptanalysis

Cipher Suite Distinguishing Problem

Machine Learning

Distinguisher

Författare

Carlo Brunetta

Chalmers, Data- och informationsteknik, Nätverk och system

Pablo Picazo-Sanchez

Chalmers, Data- och informationsteknik, Informationssäkerhet

Journal of Cryptographic Engineering

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

Vol. in Press

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Beräkningsmatematik

Annan matematik

Systemvetenskap

DOI

10.1007/s13389-021-00262-x

Mer information

Senast uppdaterat

2021-07-28