This repository collects data and scripts of the paper:
Di Campi, A.M., Focardi, R., Luccio, F.L. (2022). The Revenge of Password Crackers: Automated Training of Password Cracking Tools. ESORICS 2022 [DOI]
- guesses.py: computes the number of guesses for a given password using trained masks. Requires to gunzip
training_statsgen_sorted.txt.gz - simulation_mask_attack.py: simulate a mask attack using trained masks and tested masks. Requires to gunzip both
training_statsgen.txt.gzandtesting_statsgen.txt.gz. - training_statsgen.txt.gz: masks computed by Train_D (gzipped)
- testing_statsgen.txt.gz: masks computed by Test_D (gzipped)
- uncracked_statsgen.txt.gz: masks computed by the passwords that could not be cracked using TRule2.rule rule-based attack (gzipped)
- training_statsgen_sorted.txt.gz: masks sorted by |m|/f used in guesses.py (gzipped)
- TRule.rule: Rules trained using all the rule sets provided in the hashcat distribution plus OneRuleToRuleThemAll and popular.rule of pantagrule, sorted by descending frequencies
- TRule_freq.rule: As above with frequencies
- TRule2.rule: Rules trained using the the 3726 best rules of TRule and TrainDic_D
- TRule2_freq.txt: As above with frequencies