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Data-Driven Security: Analysis, Visualization and Dashboards / Edition 1

Data-Driven Security: Analysis, Visualization and Dashboards / Edition 1

by Jay Jacobs, Bob RudisJay Jacobs
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Uncover hidden patterns of data and respond with countermeasures

Security professionals need all the tools at their disposal to increase their visibility in order to prevent security breaches and attacks. This careful guide explores two of the most powerful data analysis and visualization. You'll soon understand how to harness and wield data, from collection and storage to management and analysis as well as visualization and presentation. Using a hands-on approach with real-world examples, this book shows you how to gather feedback, measure the effectiveness of your security methods, and make better decisions.

Everything in this book will have practical application for information security professionals.

  • Helps IT and security professionals understand and use data, so they can thwart attacks and understand and visualize vulnerabilities in their networks
  • Includes more than a dozen real-world examples and hands-on exercises that demonstrate how to analyze security data and intelligence and translate that information into visualizations that make plain how to prevent attacks
  • Covers topics such as how to acquire and prepare security data, use simple statistical methods to detect malware, predict rogue behavior, correlate security events, and more
  • Written by a team of well-known experts in the field of security and data analysis

Lock down your networks, prevent hacks, and thwart malware by improving visibility into the environment, all through the power of data and Security Using Data Analysis, Visualization, and Dashboards.

Product Details

ISBN-13: 9781118793725
Publisher: Wiley
Publication date: 02/17/2014
Edition description: New Edition
Pages: 352
Product dimensions: 7.30(w) x 9.00(h) x 0.80(d)

About the Author

Jay Jacobs is the coauthor of Verizon Data Breach Investigation Reports and the cofounder of the Society of Information Risk Analysts, where he currently sits on the board of directors.

Bob Rudis is the Director of Enterprise Information Security & IT Risk Management at Liberty Mutual Insurance and was named one of the Top 25 Influencers in Information Security by Tripwire.

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Table of Contents

Introduction xv

Chapter 1 The Journey to Data-Driven Security 1

A Brief History of Learning from Data 2

Nineteenth Century Data Analysis 2

Twentieth Century Data Analysis 3

Twenty-First Century Data Analysis 4

Gathering Data Analysis Skills 5

Domain Expertise 6

Programming Skills 8

Data Management 10

Statistics 12

Visualization (aka Communication) 14

Combining the Skills 15

Centering on a Question 16

Creating a Good Research Question 17

Exploratory Data Analysis 18

Summary 18

Recommended Reading 19

Chapter 2 Building Your Analytics Toolbox: A Primer on Using R and Python for Security Analysis 21

Why Python? Why R? And Why Both? 22

Why Python? 23

Why R? 23

Why Both? 24

Jumpstarting Your Python Analytics with Canopy 24

Understanding the Python Data Analysis and Visualization Ecosystem 25

Setting Up Your R Environment 29

Introducing Data Frames 33

Organizing Analyses 36

Summary 37

Recommended Reading 38

Chapter 3 Learning the "Hello World" of Security Data Analysis 39

Solving a Problem 40

Getting Data41

Reading In Data 43

Exploring Data 47

Homing In on a Question 58

Summary 70

Recommended Reading 70

Chapter 4 Performing Exploratory Security Data Analysis 71

Dissecting the IP Address73

Representing IP Addresses 73

Segmenting and Grouping IP Addresses 75

Locating IP Addresses 77

Augmenting IP Address Data80

Association/Correlation, Causation, and Security Operations Center Analysts Gone Rogue 86

Mapping Outside the Continents90

Visualizing the ZeuS Botnet 92

Visualizing Your Firewall Data 98

Summary 100

Recommended Reading101

Chapter 5 From Maps to Regression 103

Simplifying Maps 105

How Many ZeroAccess Infections per Country? 108

Changing the Scope of Your Data 111

The Potwin Effect 113

Is This Weird? 117

Counting in Counties 120

Moving Down to Counties 122

Introducing Linear Regression 125

Understanding Common Pitfalls in Regression Analysis 130

Regression on ZeroAccess Infections 131

Summary 136

Recommended Reading 136

Chapter 6 Visualizing Security Data 137

Why Visualize? 138

Unraveling Visual Perception 139

Understanding the Components of Visual Communications 144

Avoiding the Third Dimension 144

Using Color 146

Putting It All Together 148

Communicating Distributions 154

Visualizing Time Series 156

Experiment on Your Own 157

Turning Your Data into a Movie Star 158

Summary 159

Recommended Reading 160

Chapter 7 Learning from Security Breaches 161

Setting Up the Research 162

Considerations in a Data Collection Framework 164

Aiming for Objective Answers 164

Limiting Possible Answers 164

Allowing "Other," and "Unknown" Options 164

Avoiding Conflation and Merging the Minutiae 165

An Introduction to VERIS 166

Incident Tracking 168

Threat Actor 168

Threat Actions 169

Information Assets 173

Attributes 173

Discovery/Response 176

Impact 176

Victim 177

Indicators 179

Extending VERIS with Plus 179

Seeing VERIS in Action 179

Working with VCDB Data 181

Getting the Most Out of VERIS Data 185

Summary 189

Recommended Reading 189

Chapter 8 Breaking Up with Your Relational Database 191

Realizing the Container Has Constraints 195

Constrained by Schema 196

Constrained by Storage 198

Constrained by RAM 199

Constrained by Data 200

Exploring Alternative Data Stores 200

BerkeleyDB 201

Redis 203

Hive 207

MongoDB 210

Special Purpose Databases 214

Summary 215

Recommended Reading 216

Chapter 9 Demystifying Machine Learning 217

Detecting Malware 218

Developing a Machine Learning Algorithm 220

Validating the Algorithm 221

Implementing the Algorithm 222

Benefiting from Machine Learning 226

Answering Questions with Machine Learning 226

Measuring Good Performance 227

Selecting Features 228

Validating Your Model 230

Specific Learning Methods 230

Supervised 231

Unsupervised 234

Hands On: Clustering Breach Data 236

Multidimensional Scaling on Victim Industries 238

Hierarchical Clustering on Victim Industries 240

Summary 242

Recommended Reading 243

Chapter 10 Designing Effective Security Dashboards 245

What Is a Dashboard, Anyway? 246

A Dashboard Is Not an Automobile 246

A Dashboard Is Not a Report 248

A Dashboard Is Not a Moving Van 251

A Dashboard Is Not an Art Show 253

Communicating and Managing "Security" through Dashboards 258

Lending a Hand to Handlers 258

Raising Dashboard Awareness 260

The Devil (and Incident Response Delays) Is in the Details 262

Projecting "Security" 263

Summary 267

Recommended Reading 267

Chapter 11 Building Interactive Security Visualizations 269

Moving from Static to Interactive270

Interaction for Augmentation 271

Interaction for Exploration 274

Interaction for Illumination 276

Developing Interactive Visualizations 281

Building Interactive Dashboards with Tableau 281

Building Browser-Based Visualizations with D3 284

Summary 294

Recommended Reading 295

Chapter 12 Moving Toward Data-Driven Security 297

Moving Yourself toward Data-Driven Security 298

The Hacker 299

The Statistician 302

The Security Domain Expert 302

The Danger Zone 303

Moving Your Organization toward Data-Driven Security 303

Ask Questions That Have Objective Answers 304

Find and Collect Relevant Data 304

Learn through Iteration 305

Find Statistics 306

Summary 308

Recommended Reading 308

Appendix A Resources and Tools 309

Appendix B References 313

Index 321

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