Descriptive Outline of Advanced Behavior viii Why this Book Is Urgent xii Preface xiv Acronyms and Key Terms xix PART ONE Foundations of Advanced Network Forensics Using a Cyber-Behavioral Science Approach CHAPTER 1 Introduction to Advanced Network Forensics 3 Introduces the shift from reactive, artifact-centric forensics to behavior-based, interdisciplinary models that blend cyberpsychology, ML/AI, and legal-ethical frameworks. CHAPTER 2 Fundamentals of Network Forensics and Log Analysis 23 Establishes the core concepts, data sources, and workflows for packet, flow, and log forensics, including capture, DPI, parsing, and evidence preservation. CHAPTER 3 Building a Network Forensics Lab Environment 44 Guides the design of a safe, high-fidelity network forensics lab for adversary emulation, realistic traffic generation, and legally defensible evidence handling. PART TWO Human Factors and Behavioral Intelligence CHAPTER 4 Behavioral Analysis in Cybersecurity 69 Explores forensic cyberpsychology, profiling, emotion, and group dynamics to understand and anticipate the human factors driving cyber threats. CHAPTER 5 Cyber Forensics Behavioral Analysis (CFBA) 87 Presents the CFBA model, integrating behavioral science, digital forensics, and ML-driven scoring (CBAM/CBS) to predict and attribute cyber threats. PART THREE Advanced Techniques, Tools, and AI CHAPTER 6 Advanced Techniques and Tools 107 Details advanced behavioral defenses such as deception networks, honeypots, DPI/NTA, insider-threat analytics, and psychological profiling for proactive threat hunting. CHAPTER 7 Machine Learning and Artificial Intelligence in Forensics 124 Examines unsupervised anomaly detection, time-series forecasting, MAESTRO, and XAI as AI/ML building blocks for proactive, behavior-based forensic workflows. PART FOUR Behavioral Telemetry, SIEM, and Log Pipelines CHAPTER 8 SIEM Configuration for Behavioral Log Collection 149 Shows how to configure SIEM and UEBA using standardized telemetry, enrichment, ML baselines, and SOAR to support behavior-driven detection at scale.

CHAPTER 9 Collecting Log Streams from Enterprise, Edge, Honeypots, and Authentication Logs 163 Describes hybrid log-collection strategies across enterprise, edge, honeypots, and identity systems, including AI-powered honeypots and tiered storage models. CHAPTER 10 Correlation and Investigation within SIEM 182 Explains AI-driven SIEM correlation and investigation, combining UEBA, ML, NLP, and SOAR playbooks to automate triage and shorten MTTD/MTTR. PART FIVE Domains and Applied Behavioral Forensics CHAPTER 11 Specialized Forensics by Domain 203 Surveys domain-specific forensic frameworks and behavioral indicators for ICS/SCADA, IoT, cloud, storage, smart cities, healthcare, and interdependent infrastructures. CHAPTER 12 Case Studies in Advanced Behavior-Based Network Forensics 219 Uses real-world case studies to show CFBA, ABS, and CBFM in action, demonstrating how behavior-driven analytics reduce dwell time and prevent full-scale breaches. PART SIX Futures, Strategy, and Emerging Paradigms CHAPTER 13 The Future of Network Forensics and Cyberpsychology 237 Looks ahead to AI-scaled evidence processing, adaptive deception (SPADE), cognitive forensics, quantum-enabled analysis, and emerging ethical and legal standards. Glossary of Technical and Behavioral Terms 249 References 252 Index 267.