Improved the Automated Evaluation Algorithm Against Differential Attacks and Its Application to WARP

EasyChair Preprint 8736

Abstract

This paper presents a heuristic approach to find the key recovery-friendly distinguishers for block ciphers, which aims to attack more rounds with a lower complexity. Firstly, we construct an SAT model to search for a set of distinguishers with the minimum number of active input-output words (and optimal probability). Subsequently, based on the discovered distinguishers, we select the advantageous distinguisher with fewer key bits involved in the key recovery phase. Finally, the guess-and-check for the key recovery attack is performed using the manual approach to compute the attack parameters accurately. By applying our new technique to $\mathtt{WARP}$ proposed in SAC 2020, we identify some 19-round and 20-round advantageous differentials. Simultaneously, the high-probability chain of Sbox leads to a stronger clustering effect of the differential trails for $\mathtt{WARP}$, so we effectively improve the probability of the advantageous distinguisher. Also, we perform the first 25-round differential attacks by extending a 19-round and a 20-round distinguisher, respectively. The results cover 2 more rounds than the previous known differential attacks.

Keyphrases: Clustering effect, SAT/SMT Model, WARP, differential attack

@booklet{EasyChair:8736,
  author    = {Jiali Shi and Guoqiang Liu and Chao Li},
  title     = {Improved the Automated Evaluation Algorithm Against Differential Attacks and Its Application to WARP},
  howpublished = {EasyChair Preprint 8736},
  year      = {EasyChair, 2022}}