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Advanced Windows Memory Dump Analysis with Data Structures : Training Course Transcript and WinDbg Practice Exercises with Notes, Fifth Edition - Dmitry Vostokov

Advanced Windows Memory Dump Analysis with Data Structures

Training Course Transcript and WinDbg Practice Exercises with Notes, Fifth Edition

By: Dmitry Vostokov, Software Diagnostics Services, Dublin School of Security

Paperback | 7 May 2025 | Edition Number 5

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The full transcript of the Software Diagnostics Services training course with 16 step-by-step exercises, notes, and selected questions and answers. Learn how to navigate through memory dump space and Windows data structures to diagnose, troubleshoot, and debug complex software incidents. The training uses a unique and innovative pattern-oriented analysis approach to speed up the learning curve. It consists of practical step-by-step exercises using WinDbg to diagnose structural and behavioral patterns in the 64-bit kernel and complete (physical) memory dumps. Additional topics include memory search, kernel linked list navigation, practical WinDbg scripting, registry, system variables and objects, device drivers, and I/O. Prerequisites are basic and intermediate level Windows memory dump analysis: the ability to list processors, processes, threads, modules, apply symbols, walk through stack traces and raw stack data, diagnose patterns such as heap corruption, CPU spike, memory leaks, access violation, wait chains, and deadlocks. If you are uncomfortable with prerequisites, the Accelerated Windows Memory Dump Analysis training book is recommended before purchasing and reading this course. Audience: Software technical support and escalation engineers, system administrators, security researchers, reverse engineers, malware and memory forensics analysts, software developers, and quality assurance engineers. The fifth edition uses the latest WinDbg, includes the relevant Unified Modeling Language tutorial, revised and extended existing exercises, and adds an exercise that demonstrates the use of a Generative AI LLM assistant.

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