1. A Pilot Project on the
Use of Prediction Markets
in Information Security
Dan Geer, In-Q-Tel
Alex Hutton, Verizon Business
Greg Shannon, Carnegie Mellon
April 20th, 2011
alpha-pilot at securitypredictions dot com

2. Overview
 Motivation (dg)
 Prediction Market Examples (gs)
 What is the pilot; what information will it generate? (gs)
 Why is this valuable to the infosec industry? (ah)
 How is this helpful to security teams and professionals? (ah)
Geer Hutton Shannon Pilot Project for an InfoSec Prediction Market April 2011 2

3. Motivations
 Our Goal: Accelerated aggregation and dissemination of
actionable security information from diverse sources
 Purpose of this talk: Explain the Pilot Project
 Purpose of the pilot: Validate that we can use a market to
collect informed opinions from participants that when
aggregated and shared is of interest to individuals,
organizations and the information security industry.
 Excellent overview and references in:
 "Using Prediction Markets to Enhance US Intelligence Capabilities," CIA Center for
the Study of Intelligence, 2006, v50 n6, PDF 17pp. http://tinyurl.com/6kdqpl
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4. The Art in Prediction
 In prediction markets, the art is selecting the questions,
i.e., prediction markets are invulnerable to idiots but not
to idiotic questions.
 Science and practice alike have shown that prediction
markets have greater accuracy than surveys and, unlike
surveys, can be run continuously.
 As the rewards available to market participants rise, the
precision of the market's predictions improves.
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5. Primer
Successful Public Prediction Markets
Geer Hutton Shannon Pilot Project for an InfoSec Prediction Market April 2011 5

6. A Simple Market Example
 http://en.wikipedia.org/wiki/Prediction_market
 Will candidate X win election Y? Yes or no?
 Three elements: Participants, Contracts, Incentives
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7. Primer
What are Prediction Markets?
Large groups of people are smarter than an elite few,
no matter how brilliant — better at solving problems,
fostering innovation, coming to wise decisions,
even predicting the future.
— James Surowiecki, author of The Wisdom of Crowds
def. Speculative markets used to make predictions of specific
events. Contracts representing the event, or outcome, are
bought and sold resulting in contract price fluctuations. The
current price represents the current group estimate of the
likelihood of the event.
April 2011 7

8. How They Work:
Reflecting Confidence in Outcomes
 Individual answers are anonymous, market aggregates consensus
 Participants are incented to express the strength of their confidence
 Participants are rewarded based on the accuracy of their contributions
 Social collaboration and comments by question, surface root causes
April 2011 8

9. How They Work:
Revealing Early Warning Indicators
 Participants invest in stocks (buy/sell) and thus drive the price up or down.
The price reflects the crowd’s confidence in the stated outcome.
 Decision-makers receive an analytical, real-time consensus view into the true
state of key issues.
Project Aries will achieve customer acceptance by 30-Sept-2011.
Information
contained in
dropping
confidence
April 2011 9

10. Social Analytic Reports &
Decision Dashboards
Tracking changing trends in
consensus opinions
Identifying divergent opinions
among participants subgroups –
Monitor
par*cipa*on
where does the information
to
ensure
diversity
reside?
April 2011 10

11. Pilot Overview
 60-day alpha pilot
 Use Consensus Point as the market platform
 20-30 hand-picked participants
 Internal (market) recognition as the incentive
 Binary contracts varying in topic and duration
 Written by Geer, Hutton, Shannon
 Pilot objectives:
 At least 10 contracts open at all times
 20 contracts with at least 10 participants,100 trades
 Positive survey results from participants at the end
 At least 3 unclosed contracts estimating future events
 Have a contract payout on an unexpected security event
 Gain enough confidence to start a half-year beta
Geer Hutton Shannon Pilot Project for an InfoSec Prediction Market April 2011 11

12. What Do We Want To Know?
 What is the collective, anonymous, incented opinion
about actionable information security events and states of
the world?
 How accurate and stable is this opinion/knowledge?
 Can this knowledge benefit participants, 3rd parties and
the industry to improve information security?
 Can a prediction market mitigate the unavailability of
detailed operational infosec data?
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13. Criteria For Contracts
 A binary question
 Good: The market-cap leader in consumer operating systems issues a press-release on a
security-critical patch this quarter.
 Poor: The number of software vulnerabilities discovered in the most popular consumer
operating system increased this quarter over the previous quarter.
 A definitive authority on the result
 Good: government agency, public company, nationally-recognized institution
 Poor: news, an individual, on-line poll, micro-blog traffic
 A history of indisputable previous outcomes
 Good: Alerts issued, scores published, reports published
 Poor: News articles, court documents, non-public sources
 Market information is likely actionable
 Good: A disruptive OS patch is in the pipeline
 Poor: Companies will lose more data this year than last
 Morally benign
 Difficult for single entities to influence the outcome of the underlying event
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14. Candidate Contracts
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15. Other Candidate Sources & Contracts
 US-CERT alerts
 Botnet species announced
 Statistics from data breach reports
 Trends in security surveys and indexes
 Statistics from software security or controls reports
 MITRE CVE reports
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16. Criteria for Alpha Participants
 Demonstrated knowledge of information security
 At least 5 years of professional experience in such
 Diverse across
 Sectors: Government, Industry, Academic
 Verticals: Civilian Gov’t, Health, Financial, DoD, Telecom, etc.
 Layers: hosts, networks, applications, infrastructure, content
 Life cycle: creation, installation, operation, incidents, remediation
 Specialties: privacy, risk, availability, integrity, etc.
 Demographics
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17. Incentive Criteria
 Is legal
 Is sufficient to entice participants to divulge their
knowledge through market activity
 Benefits are tangible to all participants
 Not just the top performers
 Does not encourage market manipulation or spectuation
 Scales to 50 active contacts and 1,000 participants
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18. Value to the InfoSec Industry
 Opportunity for big-time benefit to the industry.
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19. Value to the InfoSec Industry
 A prediction market is a specifically framed piece of
knowledge (belief as a probability)
 What do you want knowledge about?
 Understand trends as they happen (or don’t happen)
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20. Value to the InfoSec Industry
Suggested context:
Capability to manage
(skills, resources,
asset
landscape decision quality…)
impact
landscape
risk
threat
landscape
controls
landscape
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21. Value to the InfoSec Industry
 Example: Mobile Malware
 % Mobile devices as targeted asset in 2011 DBIR
 % Mobile devices as targeted asset in 2012 DBIR
 % Mobile devices as targeted asset in 2013 DBIR
 The effect of new vulnerability research on the above contracts...
 The effect of new security technologies on the above contracts...
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22. Value to the InfoSec Industry
Suggested context:
Capability to manage
(skills, resources,
asset
landscape decision quality…)
impact
landscape
risk
threat
landscape
controls
landscape
Geer Hutton Shannon Pilot Project for an InfoSec Prediction Market April 2011 22

23. Value to InfoSec Teams and Professionals
 An internally facing prediction market can be used for
decision support
 Success/Failure of big dollar security projects
 What current projects (both security and non-security) mean
to the frequency or impact of security events
 Impact of current security events
 This breach will cost how much?
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24. Value to InfoSec Teams and Professionals
 Calibration
 Ability to better qualify the subjective evidence around us
 Ability to “mine” changes in “price” for causes
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25. Recap
 Our Goal: Accelerated aggregation and dissemination of
actionable security information from diverse sources
 To follow or join the pilot send e-mail to:
alpha-pilot at security predictions dot com
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26. On The Use of Prediction Markets in
Information Security (from src-bos program)
A tool created to help establish beliefs as probabilities, prediction markets are
speculative markets created for the purpose of understand the probability of future
events. Not widely used in Information Security, Prediction Markets may have
benefits to our industry. Dan Geer, Alex Hutton and Greg Shannon will give a
background around what prediction markets are, how they can be used by the
information security industry as a whole, and how security departments and
professionals can use them as a tool to help defend their environments.
Dan Geer is a computer security analyst and risk management specialist and
currently the chief information security officer for In-Q-Tel.
Alex Hutton is a principal for Research & Intelligence with the Verizon Business
RISK Team.
Dr. Greg Shannon is the chief scientist for the CERT® Program at Carnegie Mellon
University’s Software Engineering Institute.
http://www.sourceconference.com/boston/speakers_2011.asp#dgeer
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Market