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Cyber Security

Cyber Security. The Evolving Threat. March 28, 2019. Amanda Conklin Linda Der Schwartz Sarah Schwarzentraub. Outline. Introduction of Cyber Risk and Security Current Trends Modeling Techniques Used in Cyber S ecurity Example using Decision Trees Growing Challenges of Cyber Security.

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Cyber Security

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  1. Cyber Security The Evolving Threat March 28, 2019 Amanda Conklin Linda Der Schwartz Sarah Schwarzentraub

  2. Outline • Introduction of Cyber Risk and Security • Current Trends • Modeling Techniques Used in Cyber Security • Example using Decision Trees • Growing Challenges of Cyber Security

  3. Overview of Cyber Risk and Security • Importance of Strong Cyber Security Practices in Today’s World • What is a Cyber Insurance Policy? • Government’s Involvement with Cyber Security • Insurer’s Response • Improvement of Cyber Security Standards

  4. Evolution of Cyber Attacks

  5. Recent Data Breaches • Marriott • Equifax • JPMorgan Chase • Target • eBay • Yahoo

  6. Current Trends: Industry

  7. Current Trends: Action

  8. Current Trends: External Motives

  9. Current Trends: Internal Motives

  10. Current Trends – Types of Cyber Attacks Data from GAO Analysis of United States Computer Emergency Readiness Team and Office of Management and Budget Data for Fiscal Year 2017

  11. Predictive Analytics • What is Predictive Analytics? • How is it Applicable to Cyber Insurance? • Data Considerations • Importance of Computing Power • Future of Predictive Analytic Techniques for Cyber Security Risks

  12. Artificial Intelligence Data from geospatial images Source: Tractica @StatistaCharts

  13. Machine Learning

  14. Decision Tree Example: Impact of Loss Data Type Victim Country > $10M Integrity > $10M External Relationship <= $10M Pattern <= $10M <= $10M > $10M

  15. Decision Tree Example: Prediction predict(impact_loss, newdata = test) <= $10 Mil > $10 Mil 1 0.9797297 0.02027027 3 0.9797297 0.02027027 4 0.3333333 0.66666667 10 0.3333333 0.66666667 15 0.9797297 0.02027027 17 0.9797297 0.02027027 19 0.9797297 0.02027027 25 0.9797297 0.02027027 27 0.9797297 0.02027027 28 0.9797297 0.02027027 33 0.4444444 0.55555556 44 0.9797297 0.02027027 45 0.9797297 0.02027027 48 0.97972970.02027027 49 0.3333333 0.66666667

  16. Decision Tree Example: Prediction Confusion Matrix – Comparison summary of predicted results versus observed results in a classification model. Accuracy – fraction of instances that were correctly classified. = (79 + 3) / 99 = 0.828 or 82.8%

  17. Decision Tree Example: Data Imbalance in R • Oversample or Undersamplethe Data • Changing Prior Probabilities • Including a Loss Matrix

  18. Continuing Challenges of Cybersecurity • Constantly Evolving Nature of Cyber Security Industry • Growth of Online Assets • Growing Sophistication of Hackers • Proactive vs. Retroactive

  19. Questions?

  20. Thank You for Your Attention Amanda Conklin (309) 807 2339 aconklin@pinnacleactuaries.com Linda Der Schwartz (678) 894 7252 lder@pinnacleactuaries.com Sarah Schwarzentraub saachan89@gmail.com

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