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@ -17,8 +17,62 @@ tags:
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- use statistical distribution of passwords then use this to generate guesses
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- use statistical distribution of passwords then use this to generate guesses
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- can generate passwords that are likely to be used
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- also based off previous passwords
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- passwords can be guessed in less attempts
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- need to update our rules - e.g., how many guesses makes an attempt likely to be suspicious
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-
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- new password generate, which also provides real world indicator of password strength
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- faster password guessing
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- hackers will get in faster
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- need to be a step ahead of this
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- insight into strong but unused passwords
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- passwords get close and closer to those typically used
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## password "guessing"
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- gets faster as machines get faster (Moore's law)
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- machine learning reduces number of trials further by learning distributions of passwords
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- useful for us
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- even if we didn't do this research the hackers would
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- use passgan to detect guesses which may have come from passgan
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- can analyse the source of guesses for suspicous stuff e.g., ip, location etc
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- can analyse data from antivirus programs
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- useful for hackers
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- hackers can conquer our strategies
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## steganography
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- hiding secret messages in a medium that is not meant to be secret (e.g., image, audio, video)
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- used to hide content and reduce suspicion e.g., in forensic investigation
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- hidden message usually encryted but not in the sense of cryptography
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- goal is to decieve
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- embed noise into images
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### signal to noise
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- most signals contain noise e.g., static
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- noise carries info as the least significant bits in value
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- hiding data in an image in the least significant bits will be visually percieved as noise
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### e.g., derek uphams JSteg
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### stegnalysis
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- detecting hidden content
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- usually visually undetectable
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how
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- analyse DCT distributions
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- 
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F5 steganographic algorithm
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- developed to fool analysis of dct distributions
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-
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new password generate, which also provides real world indicator of password st
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# bigger issues
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# bigger issues
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