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MARC 21

How to measure anything in cybersecurity risk
Tag	Description
020	$a9781119892304
041	$aEng
084	$aQA76.9.A25 H83 2023
100	$aHubbard, Douglas W.
245	$aHow to measure anything in cybersecurity risk$ht
250	$ 2nd ed.
260	$aNew Jersey$bWiley$c2023
300	$axv, 345 p : ill. graph ; 24 cm
307	$bBook
505	$aHow to Measure Anything in Cybersecurity Risk Why We Chose This Topic What Is This Book About? We Need More Than Technology Part I Why Cybersecurity Needs Better Measurements for Risk Chapter 1 The One Patch Most Needed in Cybersecurity Insurance: A Canary in the Coal Mine The Global Attack Surface The Cyber Threat Response A Proposal for Cybersecurity Risk Management Notes Chapter 2 A Measurement Primer for Cybersecurity The Concept of Measurement A Taxonomy of Measurement Scales The Object of Measurement The Methods of Measurement Notes Chapter 3 The Rapid Risk Audit: Starting With a Simple Quantitative Risk Model The Setup and Terminology The Rapid Audit Steps Some Initial Sources of Data The Expert as the Instrument Supporting the Decision: Return on Controls Doing "Uncertainty Math" Visualizing Risk With a Loss Exceedance Curve Where to Go from Here Notes Chapter 4 The Single Most Important Measurement in Cybersecurity The Analysis Placebo: Why We Can't Trust Opinion Alone How You Have More Data than You Think When Algorithms Beat Experts Tools for Improving the Human Component Summary and Next Steps Notes Chapter 5 Risk Matrices, Lie Factors, Misconceptions, and Other Obstacles to Measuring Risk Scanning the Landscape: A Survey of Cybersecurity Professionals What Color Is Your Risk? The Ubiquitous-and Risky-Risk Matrix Exsupero Ursus and Other Fallacies Communication and Consensus Objections Conclusion Notes Part II Evolving the Model of Cybersecurity Risk Chapter 6 Decompose It: Unpacking the Details Decomposing the Simple One-for-One Substitution Model More Decomposition Guidelines: Clear, Observable, Useful A Hard Decomposition: Reputation Damage Conclusion Notes Chapter 7 Calibrated Estimates: How Much Do You Know Now? Introduction to Subjective Probability Calibration Exercise More Hints for Controlling Overconfidence Conceptual Obstacles to Calibration The Effects of Calibration Beyond Initial Calibration Training: More Methods for Improving Subjective Judgment Notes Answers to Trivia Questions for Calibration Exercise Chapter 8 Reducing Uncertainty with Bayesian Methods A Brief Introduction to Bayes and Probability Theory An Example from Little Data: Does Multifactor Authentication Work? Other Ways Bayes Applies Notes Chapter 9 Some Powerful Methods Based on Bayes Computing Frequencies with (Very) Few Data Points: The Beta Distribution Decomposing Probabilities with Many Conditions Reducing Uncertainty Further and When to Do It More Advanced Modeling Considerations Wrapping Up Bayes Notes
650	$aCyberterrorism
650	$aCyberspace--security measures
700	$aSeiersen, Richard