Interleaving Cryptanalytic Time-Memory Trade-Offs on Non-uniform Distributions
Gildas Avoine, Xavier Carpent, Cédric Lauradoux · Lecture notes in computer science · 2015
Cryptanalytic time-memory trade-offs (TMTO) are famous tools available in any security expert toolbox. They have been used to break ciphers such as A5/1, but their efficiency to crack passwords made them even more popular in the security community. While symmetric keys are generated randomly according to a uniform distribution, passwords chosen by users are in practice far from being random, as confirmed by recent leakage of databases. Unfortunately, the technique used to build TMTOs is not appropriate to deal with non-uniform distributions. In this paper, we introduce an efficient construction that consists in partitioning the search set into subsets of close densities, and a strategy to explore the TMTOs associated to the subsets based on an interleaved traversal. This approach results in a significant improvement compared to currently used TMTOs. We experimented our approach on a classical problem, namely cracking 7-character NTLM Hash passwords using an alphabet with 34 special characters. This resulted in speedups ranging from 16 to 76 (depending on the input distribution) over rainbow tables, which are considered as the most efficient variant of time-memory trade-offs. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.