دانلود کتاب اصول، جرم‌یابی پیشرفته شبکه رویکردی مبتنی بر علوم رفتاری سایبری

This book is urgent. Network defense is out of sync with modern attacks. Most organizations rely on post-incident forensics and signature-based tools. Adversaries now use human-driven and behavioral campaigns. They remain hidden amid normal traffic and user activity between alerts. Across the chapters, Advanced Behavior-Based Network Forensics argues that you either evolve toward behavior-driven, psychology aware, AI-assisted forensics, or you accept that determined attackers will live in your environment for weeks or months undetected. -Traditional methods are painfully slow and blind. Classic network forensics and signature-based IDS/AV only catch known threats, but only after compromise. They ignore the human and behavioral context behind most attacks, such as insider threats, social engineering, and slow lateral movement. -Threats are behavioral and urgent, not just technical. Ransomware operators, APTs, and insiders exploit living-off-the-land techniques, encrypted channels, and social engineering. Patterns must be exposed by modeling behavior over time, not simply searching for a malware hash or bad IP. -You need prediction, not just reconstruction. The Cyber Forensics Behavioral Analysis (CFBA) model and Adversarial Behavioral Score (ABS) can predict malicious intent up to about 45 days in advance and achieve >90% precision in some attribution tasks, providing a significant lead time compared to legacy tools that only alert once damage is underway. -Manual forensics is impossible due to the volume and -complexity of the data. Automated capture, parsing, and ML/AI-driven triage are needed for petabyte-scale logs, cloud, edge, IoT, and encrypted traffic. -Never experiment on production. Build air-gapped forensics labs with realistic traffic and adversary emulation to prove behavior-based detections before deployment.