Book Image

How to Measure Anything in Cybersecurity Risk

By : Douglas W. Hubbard, Richard Seiersen
Book Image

How to Measure Anything in Cybersecurity Risk

By: Douglas W. Hubbard, Richard Seiersen

Overview of this book

How to Measure Anything in Cybersecurity Risk exposes the shortcomings of current “risk management” practices, and offers a series of improvement techniques that help you fill the holes and ramp up security. In his bestselling book How to Measure Anything, author Douglas W. Hubbard opened the business world’s eyes to the critical need for better measurement. This book expands upon that premise and draws from The Failure of Risk Management to sound the alarm in the cybersecurity realm. Some of the field’s premier risk management approaches actually create more risk than they mitigate, and questionable methods have been duplicated across industries and embedded in the products accepted as gospel. This book sheds light on these blatant risks and provides alternate techniques that can help improve your current situation. You’ll also learn which approaches are too risky to save and are actually more damaging than a total lack of any security. Dangerous risk management methods abound; there is no industry more critically in need of solutions than cybersecurity. This book provides solutions where they exist and advises when to change tracks entirely.
Table of Contents (12 chapters)
Free Chapter
1
Foreword
2
Foreword
3
Acknowledgments
4
About the Authors
9
Index
10
EULA

Distribution Name: Power Law

Graph: horizontal axis of 0-5 million has descending curve that descends sharply to one million, becoming stable towards end. 90% area marked between 0-2 million.

Figure A.6 Power Law Distribution

Parameters:

  • Alpha (Shape parameter)
  • Theta (Location parameter)

The power law is a useful distribution for describing phenomena with extreme, catastrophic possibilities—even more than lognormal. For events such as forest fires, the vast majority of occurrences are limited to an acre or less in scope. On rare occasions, however, a forest fire may spread over hundreds of acres. The “fat tail” of the power law distribution allows us to acknowledge the common small event, while still accounting for more extreme possibilities.

  • When to Use: When you want to make sure that catastrophic events, while rare, will be given nontrivial probabilities.
  • Examples: Phenomena like earthquakes, power outages, epidemics, and other types of “cascade failures” have this property.
  • Excel Formula: =(theta/x)^alpha
  • Mean: =(alpha*theta/(alpha-1))