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Something Wicked

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Something Wicked: Defensible Social Architecture in the context of Big Data, Behavioral Econ, Bot Hives, and Bad Actors. BSides Las Vegas 2017 keynote presentation from Allison Miller (@selenakyle)

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Something Wicked

  1. 1. Something Wicked Defensible Social Architecture in the context of Big Data, Behavioral Econ, Bot Hives, and Bad Actors allison miller @selenakyle
  2. 2. a world of firewalls & moats
  3. 3. soylent perimeter is people
  4. 4. scammers spammers fraud bots phishing DOS malware griefers hijacking sniffing @selenakyle
  5. 5. troll thunderdrome
  6. 6. from behavior to tech to behavior
  7. 7. decision science + applied economics
  8. 8. behavior data analytics economics preferences incentives
  9. 9. system theory + machine learning = smarter social systems
  10. 10. data rules everything around me
  11. 11. modeling + feedback driving risk to a chokepoint decisions have cost quantified performance
  12. 12. models + feedback
  13. 13. UX Pla>orm Back Office Event Post-decision UX Post-Txn Experience Models* Decision Strategy Score / Policy Verdict Response Post-event acKons Post-event data Data aggregates (txn & acct records) Model training, tesKng, & builds
  14. 14. * speaking ground truth to power
  15. 15. * speaking ground truth to power
  16. 16. * speaking ground truth to power
  17. 17. decisions & cost
  18. 18. Authorize Block Good false positive Bad false negative Good Action Gets Blocked Bad Action Gets Through Downstream Impacts
  19. 19. quantified performance
  20. 20. % Bad is True % Total
  21. 21. Gain % Bad is True % Total
  22. 22. Gain % Bad is True % Total
  23. 23. modeling + feedback driving risk to a chokepoint decisions have cost quantified performance
  24. 24. preferential treatments
  25. 25. no, YOU’RE irrational
  26. 26. behavioral... ...finance ...economics ...game theory
  27. 27. choice architecture opinionated design data devaluation ...competition
  28. 28. framing + anchoring
  29. 29. opinionated design are you sure?
  30. 30. opinionated design let’s not.
  31. 31. data devaluation
  32. 32. choice architecture opinionated design data devaluation ...other system agents
  33. 33. dismal scienceing
  34. 34. Microeconomics •  Model for estimating consumption given individual preferences, under a budget constraint •  Utility maximization •  Preferences: Consumption mix •  Good A vs Good B •  Labor vs leisure •  Budget constraint
  35. 35. Positive Normative What it is What it should be Descriptions Recommendations
  36. 36. Themes of Security Economics •  Security ROI •  Cybercrime supply chains •  Market for Lemons •  Make it more expensive for the attacker •  Tragedy of the Commons •  Risk Tolerance •  Exploit/Vuln markets •  Behavioral Economics / Gamification
  37. 37. Wicked Games Preferences Utility Money Returns Competition Tolerances Uncertainty Data Returns Adversaries Policy Analysis Graph Theory Dynamic Threat Models Cyberinsurance Security Econometrics Classification Inferior Goods Security “CPI” Incentive Design Coalitional Game Theory @selenakyle
  38. 38. Toolkit to Consider Security ROI à Tradeoff distributions Security Poverty Line à Inferior Goods Information Asymmetries à Signaling Repeated Games à Coalitional Game Theory Risk management à Going for V(X) vs E(X) @selenakyle
  39. 39. coalitional game theory consumption & maturity signal development omg risk
  40. 40. (you’ve just been risk-rolled btw) Concept where theory meets behavior •  Expected value vs expected variance •  Probability gives you both, we tend to focus on E(x) •  Risk aversion is a condition that relies on V(x)
  41. 41. Payoffs UP DOWN CIRCLE RED BLUE MARIO LUIGI KIRBY GIZMO 10, 3 2, 10 2, 5 -3, 3 A B B A A A
  42. 42. An example You have $20k, but a 50/50 chance of losing $10k •  Expected value? •  $15k (i.e. .5($20k)+.5($10k)) Insurance costing $5k will cover full loss. Should you buy it or not? •  E(X) w/insurance? à $15k (for sure) •  E(X) w/o insurance à $15k (but as EITHER $10k or $20k) A risk averse individual will opt for the same E(X) w/less uncertainty (less risk) •  People seek utility maximization, not payoffs •  Risk, i.e. uncertainty, reduces overall utility (wealth)
  43. 43. An example…continued You have $20k, but a 50/50 chance of losing $10k •  E(X)= $15k You are offered partial insurance costing $2.5k will cover half of the loss ($5k). @ No Loss: $17.5k ($20k – 2.5k) @ Loss: $12.5k ($20k – 2.5k – 10k – 5k) •  Expected value = $15k (but as EITHER $17.5k or $12.5k) Risk, i.e. uncertainty, is reduced but there is still a $5k variance
  44. 44. #BOOMTIME #RISK @SELENAKYLE What this Looks like Utility Wealth E(V) U(total) U(partial) U(no insurance) 12.5 17.515
  45. 45. The language of risk •  Some optimization functions assume *certainty* •  But making decisions under uncertainty is core to: •  Competition •  Investment •  Reality •  How are we talking about risk? Focus on E(X) or V(X)?
  46. 46. How to Win at Risk Win or lose? •  Game theory approach: maximize payoff …Tends to gravitate towards expected value •  The “defender’s dilemma” assumes a risk intolerant system manager …Lower expected loss. •  Optimal investments manage to value and variance …Build systems with better risk capacity …Portfolio theory, not just point performance
  47. 47. <regroup>
  48. 48. humans: how do they work?
  49. 49. humans: how do they work? are you sure?
  50. 50. by the pricking of my thumbs, something wicked this way comes. open, locks, whoever knocks.
  51. 51. Thank You BSidesLV! Allison Miller @selenakyle
  52. 52. . Some references (mostly about behavior) •  Improving SSL Warnings: Comprehension and Adherence. A. Felt, A. Ainslie, R. Reeder, S. Consolvo, S. Thyagaraja, A. Bettes, H. Harris, and J. Grimes. CHI, page 2893-2902. ACM, (2015)  https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43265.pdf •  D. Akhawe and A. P. Felt. Alice in warningland: A large-scale field study of browser security warning effectiveness. In Proc. of USENIX Security, pages 257–272, 2013. •  Ariely, Dan. Predictably Irrational: The Hidden Forces That Shape Our Decisions. New York, NY: HarperCollins, 2008.  •  Arthur, W Brian. All Systems will be Gamed: Exploitive Behavior in Economic and Social Systems.  Santa Fe Institute: SFI WORKING PAPER: 2014-06-016.  http://tuvalu.santafe.edu/~wbarthur/Papers/All%20Systems%20Gamed.pdf To appear in Complexity and the Economy, W. B Arthur, Oxford University Press, 2014 •  Kahneman, Daniel. Thinking, Fast and Slow. New York: Farrar, Straus and Giroux, 2011.  •  Leyton-Brown, Kevin, and Yoav Shoham. Essentials of Game Theory: A Concise, Multidisciplinary Introduction. [San Rafael, Calif.]: Morgan & Claypool, 2008.  •  Meadows, Donella. Thinking in Systems: A Primer. London: Chelsea Green Publishing, 2008.
  53. 53. . More references (mostly about decisions & game theory) •  Axelrod, Robert M. The Evolution of Cooperation. New York: Basic, 1984.  •  Dixit, Avinash and Nalebuff, Barry. The Art of Strategy: A Game Theorist’s Guide to Success in Business and in Life.  •  Dormehl, Luke. Thinking Machines. Tarcher Perigee. New York, 2017. •  Fisher, Len. Rock, Paper, Scissors: Game Theory in Everyday Life. New York: Basic, 2008.  •  Gibbons, Robert. Game Theory for Applied Economists. Princeton, NJ: Princeton UP, 1992.  •  Gintis, Herbert. Game Theory Evolving: A Problem-centered Introduction to Modeling Strategic Behavior. Princeton, NJ: Princeton UP, 2000.  •  Ignacio Palacios-Heurta (2003) “Professionals Play Minimax”. Review of Economic Studies, Volume 70, pp 395-415.  •  Jackson, Leyton-Brown & Shoham. Game Theory. (Stanford University and University of British Columbia: Coursera), http://www.coursera.org, Accessed 2013.  •  Polak, Ben. Game Theory (Yale University: Open Yale Courses),  http://oyc.yale.edu, Accessed 2012. License: Creative Commons BY-NC-SA Thomas, L. C. Games, Theory, and Applications. Chichester: E. Horwood, 1984.  •  Wikipedia’s sections on Game Theory, Economics, & Probability.
  54. 54. . Even more references (mostly about security economics) •  Anderson, Ross. "Risk and Privacy Implications of Consumer Payment Innovation." PDF available at http://www.cl.cam.ac.uk/~rja14/Papers/anderson-frb-kansas-mar27.pdf  Paper presented at "Consumer Payment Innovation in the Connected Age" conference took place March 29-30, 2012, in Kansas City, Mo. •  Anderson, Ross, Chris Barton, Rainer Bohme, Richard Clayton, Michel J.G. van Eeten, Michael Levi, Tyler Moore, and Stefan Savage. “Measuring the cost of cybercrime”. (2012) PDF available at:  http://weis2012.econinfosec.org/papers/Anderson_WEIS2012.pdf.  •  Camp, L. Jean and Wolfram, Catherine D., “Pricing Security: Vulnerabilities as Externalities.” Economics of Information Security, Vol. 12, 2004. Available at SSRN: http://ssrn.com/abstract=894966. •  MacCarthy, Mark. “Information Security Policy in the U.S. Retail Payments Industry.” Workshop on the Economics of Information Security, June 2010. •  Sullivan, Richard J. “The Changing Nature of U.S. Card Payment Fraud: Issues for Industry and Public Policy.” For presentation at the 2010 Workshop of Economics of Information Security. May 21, 2010. •  Skeen, Dale. "How Real-Time Analytics Protects Banks from Large Scale Cyber Attacks." Information Security Buzz, accessed Feb 23, 2014.  http://www.informationsecuritybuzz.com/real-time-analytics-protects-banks-large-scale-cyberattacks/ •  Varian, Hal. “Managing Online Security Risks.” New York Times; New York, N.Y.; Jun 1,2000.

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