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                            Library & Multimedia
 Password Cracking Using Probabilistic Context-Free Grammars
NCJ Number
              308111

Author(s)
          Matt Weir;
                        Sudhir Aggarwal;
                        Breno de Medeiros;
                        Bill Glodek
Date Published

2009
Length
              15 pages
Annotation
              This paper outlines a new method for cracking passwords using probabilistic context-free grammars.
Abstract
              In this paper the authors discuss a new method that generates password structures in highest probability order. The authors will also show that this approach seems to provide a more effective way to crack passwords as compared to traditional methods by testing their tools and techniques on real password sets. Choosing the most effective word-mangling rules to use when performing a dictionary-based password cracking attack can be a difficult task. The authors first automatically create a probabilistic context-free grammar based upon a training set of previously disclosed passwords. This grammar then allows us to generate word-mangling rules, and from them, password guesses to be used in password cracking. In one series of experiments, training on a set of disclosed passwords, this approach was able to crack 28% to 129% more passwords than John the Ripper, a publicly available standard password cracking program. (Published Abstract Provided)
      
  
          Additional Details
Grant Number(s)
        2006-DN-BX-K007


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Report (Study/Research)


  
  
        
  Award
        E-Crime Investigative Technologies
    
  
  
Date Published: January 1, 2009
    
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