03-16-2015, 11:20 PM
Hey I have to say what great software! I have seen it in the past but never had a chance to try it out. Hats off to the devs and to the support staff on the forums most my questions were clearly addressed prior to me even asking them from the forums.
I do have a question on opinion and Im sure the response will be "It depends" based off the target passwords I need to get.
But what is the best method of approaching breaking passwords. Example:
Right now here is how I am cracking passwords:
List of human-passwords no rules.
List of human-passwords with \d\d at the end.
List of words no rules.
Lust of words with \d\d at the end.
Is there a better generalized method for getting more passwords on a first few passes? Should I mix up my order? (25 mins on my rig)
Currently my human-password list is 946MB in size and is made of Gmail 5 million leak, and 10 Million example passwords publication. I have stripped out usernames and emails so its purely the passwords. (4 hours on my rig)
My word list is a collection of people places and things (movies books etc). Its about 5GB and contains only lower case words.
Currently I am doing research into distributing the cracking across a number of workstations \ into my ESX cluster but I hear GPU is the way to go.
I do have a question on opinion and Im sure the response will be "It depends" based off the target passwords I need to get.
But what is the best method of approaching breaking passwords. Example:
Right now here is how I am cracking passwords:
List of human-passwords no rules.
List of human-passwords with \d\d at the end.
List of words no rules.
Lust of words with \d\d at the end.
Is there a better generalized method for getting more passwords on a first few passes? Should I mix up my order? (25 mins on my rig)
Currently my human-password list is 946MB in size and is made of Gmail 5 million leak, and 10 Million example passwords publication. I have stripped out usernames and emails so its purely the passwords. (4 hours on my rig)
My word list is a collection of people places and things (movies books etc). Its about 5GB and contains only lower case words.
Currently I am doing research into distributing the cracking across a number of workstations \ into my ESX cluster but I hear GPU is the way to go.