The Metrics Manifesto: Confronting Security with Data
- Length: 320 pages
- Edition: 1
- Language: English
- Publisher: Wiley
- Publication Date: 2022-05-10
- ISBN-10: 111951536X
- ISBN-13: 9781119515364
- Sales Rank: #7084 (See Top 100 Books)
This book is predictive security metrics with R. It is a quantitative shift in security strategy and tactics that looks to the sciences, professional sports, and others for cues on measuring improvement (security, predictive analytics, and programming in R).
The ultimate goal of this book is to show the truths about a corporation’s security programs. This is done by confronting the program with data. That means the data coming from the program should unambiguously prove whether or not the technology team is improving the program. This book will be tool to be used in discovery how to improve IT security procedures.
Cover Table of Contents Title Page Copyright Dedication Foreword Preface: How This Book Came to Be About the Technical Review Team Robert D. Brown III Anuj Gargeya Malkapuram Kaela Seiersen CHAPTER 1: Introduction: The Manifesto and the BOOM! Framework What's Next: Caveats and Epiphanies The Metrics Manifesto and BOOM! The (Modern) Metrics Manifesto BOOM: Baseline Objectives and Optimization Measurements Bullet Holes and Bombers BOOM Defined Notes CHAPTER 2: Time to Event Metrics Threat Hunting with Dr. Snow From Cholera to Security Life Tables Making a Life Table in R Code Explained in Detail The Survival Functions Life Table Detail Basic Life Table Metrics Conclusion Notes CHAPTER 3: Counting on Uncertainty Gamblers, Scientists, and a Theologian The Persistence and Dominance of Bayes A Bayesian Primer Metrics Example: Phishing for Improvement From ABC to Canonical Bayes Notes CHAPTER 4: Burndown Rates: Shifting Right the Bayesian Way The Day 1 Metric Graph the Updated Model Final: Comparing Teams Wrapping Up Notes CHAPTER 5: Risk Arrival Rates: Shift Left Security Metrics Introduction: Random Bombs and Horse Kicks From Burndown to Arrival Simulating Arrivals Bayes Meets Arrival Rates Advanced Prediction Notes CHAPTER 6: Wait-Time Rates: Between Arrival and Departure Is…Waiting Bayesian Wait-Times Mitigatable Surprise NVD Analysis Decomposed Summary Notes CHAPTER 7: Escape Rates What Is an Escape Rate? Naive Escape Rates Functional Decomposition Escape Rates in 10 Lines of Code Chapter Summary Notes CHAPTER 8: Optimization Basics with Bayesian Linear Regression Grid Approximation Steps Toward Optimization: Using MCMC-Based Regression Markov Chain Monte Carlo (MCMC) Conceptual Primer A Brief Introduction to Regression Analysis Final Chapter Thoughts Notes CHAPTER 9: ABC A/B Testing and Security ROI Get Better ROI for Security Buying Security with Predictive Analytics The Use Case: Web Application and API Scanning Step 1: Model Your Beliefs Step 2: Mash up Data with Beliefs Step 3: Forecast the Financial Impact of Errors Code Details and Design Conclusion Notes CHAPTER 10: Dashboarding with BOOM! BOOM Metrics Objects KPI Analysis – Scoring the Scores Making Shiny Dashboards Shiny Code Conclusion Notes CHAPTER 11: Simulating Data Like a Pro Introduction Let's Make Some Data: Part 1 Let's Make Some Data: Part 2 Enriching and Tidying Notes Epilogue: A Short One-for-One Substitution Guide BOOM for CIS Metrics Focusing on Outcomes Next Steps Notes Index End User License Agreement
Donate to keep this site alive
1. Disable the AdBlock plugin. Otherwise, you may not get any links.
2. Solve the CAPTCHA.
3. Click download link.
4. Lead to download server to download.