Bayesian Modeling for Differential Cryptanalysis of Block Ciphers: A DES Instance

Encryption algorithms based on block ciphers are among the most widely adopted solutions for providing information security. Over the years, a variety of methods have been proposed to evaluate the robustness of these algorithms to different types of security attacks. One of the most effective analysis techniques is differential cryptanalysis, whose aim is to study how variations in the input propagate on the output. In this work we address the modeling of differential attacks to block cipher algorithms by defining a Bayesian framework that allows a probabilistic estimation of the secret key. In order to prove the validity of the proposed approach, we present as case study a differential attack to the Data Encryption Standard (DES) which, despite being one of the methods that has been most thoroughly analyzed, is still of great interest to the scientific community since its vulnerabilities may have implications on other ciphers.