Evolving Cybersecurity Threats and Trends
Explore the evolving landscape of cyber threats and security practices, including current trends, modeling techniques, and examples of cyber attacks. Discover the importance of strong cybersecurity measures and the impact of recent data breaches on various industries. Gain insights into internal and external motives driving cyber attacks and the role of government and insurers in improving cybersecurity standards.
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Presentation Transcript
Cyber Security The Evolving Threat Amanda Conklin Linda Der Schwartz Sarah Schwarzentraub March 28, 2019
Outline Introduction of Cyber Risk and Security Current Trends Modeling Techniques Used in Cyber Security Example using Decision Trees Growing Challenges of Cyber Security 1
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 2
Evolution of Cyber Attacks 1988 First Known Cyberattack: The Morris Worm 2000 Email Phishing 2007 Denial-of-Service Attacks 2010 Ransomware 2013 6.5 Million Malware Samples Created 2018 Cybersecurity is Estimated to Reach $96.3B 3
Recent Data Breaches Marriott Equifax JPMorgan Chase Target eBay Yahoo 4
Current Trends: Industry Industry Agriculture Mining Construction Management Real Estate Entertainment Trade Unknown Administrative Professional Transportation Accomodation Manufacturing Retail Educational Finance Other Services Utilities Information Healthcare Public 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 5
Current Trends: Action Action Malware Social Hacking Error Physical Misuse 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 6
Current Trends: External Motives Motive of External Attacks NA Convenience Fear Secondary Other Grudge Espionage Fun Unknown Ideology Financial 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% 40.00% 45.00% 50.00% 7
Current Trends: Internal Motives Motive of Internal Attacks Fear Grudge Espionage Convenience Fun Other Ideology Financial NA Unknown 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 8
Current Trends Types of Cyber Attacks Types of Cyber Attacks Attrition, External/Removable Media, Physical Cause Mutliple Attack Vectors Web Loss or Theft of Equipmnet Email/Phishing Other Improper Usage Data from GAO Analysis of United States Computer Emergency Readiness Team and Office of Management and Budget Data for Fiscal Year 2017 9
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 10
Artificial Intelligence The Future of A.I. Forecasted Cumulative Global Artifical Intelligence Revenue 2016-2025 by U.S. Dollars (in Millions) Prevention against cybersecurity threats Object detection and classification - avoidance, navigation Content distribution of social media Automated geophysical feature detection Text query of images Object identification, detection, classification, tracking Predictive maintenance Efficient, scalable processing of patient data Algorithmic trading strategy performance improvement Static image recognition, classification, and tagging Data from geospatial images Source: Tractica @StatistaCharts - 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 10,000 11
Machine Learning Machine Learning Instance based Regression Regularization Decision Trees Dimensionality Reduction Bayesian Association Rule Learning Clustering Artificial Neural Network Deep Learning 12
Decision Tree Example: Impact of Loss Data Type Victim Country > $10M > $10M Integrity External Relationship <= $10M <= $10M Pattern <= $10M > $10M 13
Decision Tree Example: Prediction predict(impact_loss, newdata = test) <= $10 Mil 0.9797297 0.9797297 0.3333333 0.3333333 0.9797297 0.9797297 0.9797297 0.9797297 0.9797297 0.9797297 0.4444444 0.9797297 0.9797297 0.9797297 0.3333333 > $10 Mil 0.02027027 0.02027027 0.66666667 0.66666667 0.02027027 0.02027027 0.02027027 0.02027027 0.02027027 0.02027027 0.55555556 0.02027027 0.02027027 0.02027027 0.66666667 1 3 4 10 15 17 19 25 27 28 33 44 45 48 49 14
Decision Tree Example: Prediction Confusion Matrix Comparison summary of predicted results versus observed results in a classification model. Predicted Predicted n = 99 n = 99 <= $10 Mil > $10 Mil <= $10 Mil > $10 Mil Actual Actual <= $10 Mil <= $10 Mil 79 79 8 8 > $10 Mil > $10 Mil 9 9 3 3 Accuracy fraction of instances that were correctly classified. = (79 + 3) / 99 = 0.828 or 82.8% 15
Decision Tree Example: Data Imbalance in R Oversample or Undersample the Data Changing Prior Probabilities Including a Loss Matrix 16
Continuing Challenges of Cybersecurity Constantly Evolving Nature of Cyber Security Industry Growth of Online Assets Growing Sophistication of Hackers Proactive vs. Retroactive 17
Questions? 18
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 Commitment Beyond Numbers 19
