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121 lines
3.4 KiB
Markdown
121 lines
3.4 KiB
Markdown
---
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title: "18-ML-in-IA-2"
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aliases:
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tags:
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- lecture
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- comp210
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sr-due: 2022-11-08
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sr-interval: 22
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sr-ease: 270
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---
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# nefarious uses of ml
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## password guessing
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- normally based on heuristics that are designed by humans
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- biased may not match true distributions of passwords
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- leaked data can be used to "learn" what to guess
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- gain insight into what users use as passwords
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## alternative - PassGan
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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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- seeded with key to create pseudorandom sequence for embedding
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- can preserve statistical properties of DCT distributions
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can use ML to find hidden images
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- then hackers will try to fool this
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- some will always get through
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# bigger issues
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- deepfakes to to shape political views of the day
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- pixel replacement with segmentation and inpainting
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- 
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## is ML good or bad
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- being used everywhere
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- should we care
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- data and modelling cannot always be 100% perfect
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- e.g., killer drones
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- privacy concerns
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- linked data
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- pipelins - information seepage
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nx integrated data infrastructure
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ethics
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- what considerations need to be made
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- ML being used to automate decision making
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- ML sentencing of criminals
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theft
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- theft of data
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- data is more valuable
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- transfer learning
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- reverse engineering a ML model
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# where to from here
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- good and bad are human constructs
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- how will laws work
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- can we use ML to make laws
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- Do we need to stop it? |