realistic synthetic generation allows secure development
TRANSCRIPT
© 2014 MapR Technologies 1© 2014 MapR Technologies
Realistic Synthetic Data Allows Secure Development
Ted Dunning
June 11, 2015
© 2014 MapR Technologies 2
Who am I?
Ted Dunning, Chief Applications Architect MapR Technologies
Email [email protected] [email protected]
Twitter @Ted_Dunning
© 2014 MapR Technologies 3
Short Books by Ted Dunning & Ellen Friedman
• Published by O’Reilly in 2014 and 2015• For sale from Amazon or O’Reilly• Free e-books currently available courtesy of MapR
http://bit.ly/ebook-real-world-hadoop
http://bit.ly/mapr-tsdb-ebook
http://bit.ly/ebook-anomaly
http://bit.ly/recommendation-ebook
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The basic idea
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Anomaly Detection and Fraud Analytics
• Financial customer wants to identify zero-day attacks
• And advanced persistent threats
• By sophisticated adversaries who don’t use known vectors
• Must keep logs and other data secret– But must also collaborate on detection algorithms
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Secure Development is Hard
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Secure Development is Hard
Outside collaborators are outside the security perimeter
They can’t see the data and they can’t tune new algorithms to fit reality
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How To Make Realistic Data
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Parametric Simulation
Parametric matching of failure signatures allows emulation of complex data properties
Matching on KPI’s and failure modes guarantees practical fidelity
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Do’s and Don’ts
• Do match the KPI’s and failure modes– Speed– Score distribution– False positive rates versus score
• Don’t try to match the actual data distribution precisely– Good enough is good enough and we want to imitate failures,
not create new life forms– Probably impossible to do precisely– Even if possible, it is vastly harder to match distributions
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Methods for Generating Numbers
• Well-known distributions– Uniform, normal, gamma, Poisson– Truncations
• Cumulations– Random walk v1
• Mixture distributions• Hyper-parameters
– Random walk v2
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Normal
data = data.frame(x=rnorm(10000), y=rnorm(10000))
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Mixture of Normals
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Random Walk
y = cumsum(rnorm(10000))
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Pick Mean from Multinomial
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Random Walk with Variable Standard Deviation
y = cumsum(rt(10000, df=0.9))
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Methods for Generating Symbols
• Symbols are really just integers with a dictionary• Well-known distributions
– Multinomial– Dirichlet processes– Rich-get-richer, Pittman-Yor
• Mixture distributions• Hyper-parameters• Lookup tables!!!
– Simple tables– Data table joins for correlated components
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Skewed Integers
207 3 203 0 198 7 196 4 195 12 193 10 189 2 187 1 185 13 179 6 178 9 177 5 177 25 174 21 173 8 173 14 170 18
[ {"name":"x", "class":"int", "skew":1}]
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Methods for Generating Behaviors
• Use structured data!– Generate user meta-data– Generate list of transactions
• Only flatten if necessary• See Apache Drill for post-processing
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Methods for Generating Databases
• Use integers (see previous) as foreign keys• Normalized form implies (approximate) independence of tables
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Go get log-synth
https://github.com/tdunning/log-synth
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A worked example...
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Simulation Setup
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Questions?
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Thank You
@mapr maprtech
[email protected]@apache.org
Ted Dunning, Chief Application Architect
MapRTechnologies
maprtech
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