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Realistic Synthetic Data Allows Secure Development

Ted Dunning

June 11, 2015

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Who am I?

Ted Dunning, Chief Applications Architect MapR Technologies

Email tdunning@mapr.com tdunning@apache.org

Twitter @Ted_Dunning

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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

tdunning@mapr.comtdunning@apache.org

Ted Dunning, Chief Application Architect

MapRTechnologies

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