scholarly journals CryptoMiniSat Switches-Optimization for Solving Cryptographic Instances

10.29007/vpd6 ◽  
2019 ◽  
Author(s):  
Anastasia Leventi-Peetz ◽  
Oliver Zendel ◽  
Werner Lennartz ◽  
Kai Weber

Performing hundreds of test runs and a source-code analysis, we empirically identified improved parameter configurations for the CryptoMiniSat (CMS) 5 for solving crypto- graphic CNF instances originating from algebraic known-plaintext attacks on 3 rounds encryption of the Small AES-64 model cipher SR(3, 4, 4, 4). We finally became able to reconstruct 64-bit long keys in under an hour real time which, to our knowledge, has never been achieved so far. Especially, not without any assumptions or previous knowledge of key-bits (for instance in the form of side-channels, as in [11]). A statistical analysis of the non-deterministic solver runtimes was carried out and command line parameter combinations were defined to yield best runtimes which ranged from under an hour to a few hours in median at the beginning. We proceeded using an Automatic Algorithm Configuration (AAC) tool to systematically extend the search for even better solver configurations with success to deliver even shorter solving times. In this work we elaborate on the systematics we followed to reach our results in a traceable and reproducible way. The ultimate focus of our investigations is to find out if CMS, when appropriately tuned, is indeed capable to attack even bigger and harder problems than the here solved ones. For the domain of cryptographic research, the duration of the solving time plays an inferior role as compared to the practical feasibility of finding a solution to the problem. The perspective scalability of the here presented results is the object of further investigations.

Author(s):  
Aymeric Blot ◽  
Holger H. Hoos ◽  
Laetitia Jourdan ◽  
Marie-Éléonore Kessaci-Marmion ◽  
Heike Trautmann

2009 ◽  
Vol 36 ◽  
pp. 267-306 ◽  
Author(s):  
F. Hutter ◽  
H. H. Hoos ◽  
K. Leyton-Brown ◽  
T. Stuetzle

The identification of performance-optimizing parameter settings is an important part of the development and application of algorithms. We describe an automatic framework for this algorithm configuration problem. More formally, we provide methods for optimizing a target algorithm’s performance on a given class of problem instances by varying a set of ordinal and/or categorical parameters. We review a family of local-search-based algorithm configuration procedures and present novel techniques for accelerating them by adaptively limiting the time spent for evaluating individual configurations. We describe the results of a comprehensive experimental evaluation of our methods, based on the configuration of prominent complete and incomplete algorithms for SAT. We also present what is, to our knowledge, the first published work on automatically configuring the CPLEX mixed integer programming solver. All the algorithms we considered had default parameter settings that were manually identified with considerable effort. Nevertheless, using our automated algorithm configuration procedures, we achieved substantial and consistent performance improvements.


2016 ◽  
Vol 3 ◽  
pp. 43-58 ◽  
Author(s):  
Manuel López-Ibáñez ◽  
Jérémie Dubois-Lacoste ◽  
Leslie Pérez Cáceres ◽  
Mauro Birattari ◽  
Thomas Stützle

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