Modern Vulnerability Management. Predictive Cybersecurity

Description

Contents Foreword Acknowledgments 1 THE STATE OF THE VULNERABILITY LANDSCAPE 1.1 THE SECURITY CANON: FUNDAMENTAL CYBERSECURITYTERMINOLOGY 1.2 SECURITY METRICS: THE NEW GUARD 2 DATA SCIENCE TO DEFINE RISK 2.1 RISK MANAGEMENT HISTORY AND CHALLENGES 3 DECISION SUPPORT: TAPPING MATHEMATICALMODELS AND MACHINE LEARNING 3.1 MATHEMATICAL MODELING 3.2 MACHINE LEARNING FOR CYBERSECURITY 4 HOW TO BUILD A DECISION ENGINE TOFORECAST RISK 4.1 THE DATA 4.2 BUILDING A LOGISTIC REGRESSION MODEL 4.3 DESIGNING A NEURAL NETWORK 5 MEASURING PERFORMANCE 5.1 RISK VS PERFORMANCE 5.2 WHAT MAKES A METRIC “GOOD”? 5.3 REMEDIATION METRICS 5.4 WHY DOES PERFORMANCE MATTER? 5.5 MEASURING WHAT MATTERS 6 BUILDING A SYSTEM FOR SCALE 6.1 CONSIDERATIONS BEFORE YOU BUILD 6.2 ON PREMISE VS CLOUD 6.3 PROCESSING CONSIDERATIONS 6.4 DATABASE ARCHITECTURE 6.5 SEARCH CAPABILITIES 6.6 ROLE-BASED ACCESS CONTROLS 7 ALIGNING INTERNAL PROCESS AND TEAMS 7.1 THE SHIFT TO A RISK-BASED APPROACH 7.2 DRIVING DOWN RISK 7.3 SLA ADHERENCE 7.4 SHIFTING FROM SECURITY-CENTRIC TO IT SELF-SERVICE 7.5 STEADY-STATE WORKFLOW 7.6 THE IMPORTANCE OF PROCESS AND TEAMS 8 REAL-WORLD EXAMPLES 8.1 A WORD FROM THE REAL WORLD1 9 THE FUTURE OF MODERN VM 9.1 STEPS TOWARD A PREDICTIVE RESPONSE TO RISK 9.2 FORECASTING VULNERABILITY EXPLOITATION WITH THE EXPLOITPREDICTION SCORING SYSTEM 9.3 SUPPORT FROM INTELLIGENT AWARENESS 9.4 THE RISE OF XDR 9.5 THE OTHER SIDE OF THE COIN: REMEDIATION 9.6 THE WICKED PROBLEM OF SECURITY ADVANCES GLOSSARY ABOUT THE AUTHORS INDEX