quartz/content/notes/18-ML-in-IA-2.md

title	aliases	tags
18-ML-in-IA-2		lecture comp210

nefarious uses of ml

password guessing

• normally based on heuristics that are designed by humans
  • biased may not match true distributions of passwords
• leaked data can be used to "learn" what to guess
• gain insight into what users use as passwords

alternative - PassGan

• use statistical distribution of passwords then use this to generate guesses

• can generate passwords that are likely to be used

• also based off previous passwords

• passwords can be guessed in less attempts

  • need to update our rules - e.g., how many guesses makes an attempt likely to be suspicious

• new password generate, which also provides real world indicator of password strength

• faster password guessing

  • hackers will get in faster
  • need to be a step ahead of this

• insight into strong but unused passwords

• passwords get close and closer to those typically used

password "guessing"

• gets faster as machines get faster (Moore's law)

• machine learning reduces number of trials further by learning distributions of passwords

• useful for us

  • even if we didn't do this research the hackers would
  • use passgan to detect guesses which may have come from passgan
  • can analyse the source of guesses for suspicous stuff e.g., ip, location etc
  • can analyse data from antivirus programs

• useful for hackers

  • hackers can conquer our strategies

steganography

• hiding secret messages in a medium that is not meant to be secret (e.g., image, audio, video)
• used to hide content and reduce suspicion e.g., in forensic investigation
• hidden message usually encryted but not in the sense of cryptography
• goal is to decieve

• embed noise into images

signal to noise

• most signals contain noise e.g., static
• noise carries info as the least significant bits in value
• hiding data in an image in the least significant bits will be visually percieved as noise

e.g., derek uphams JSteg

stegnalysis

• detecting hidden content
• usually visually undetectable

how

• analyse DCT distributions
• 

F5 steganographic algorithm

• developed to fool analysis of dct distributions

bigger issues

where to from here