This short book is a full transcript of the Software Diagnostics Services seminar about physical memory analysis on desktop and server Windows platforms (a revised version of the previous seminars on complete crash and hang memory dump analysis). Topics include memory acquisition and its tricks, user vs. kernel vs. physical memory space, fiber bundle space, challenges of physical memory analysis, common WinDbg commands, memory analysis patterns and their classification, common mistakes, a hands-on WinDbg analysis example with logs, and a guide to further study. For this new edition, slides and their transcript text have been significantly revised, links and references have been checked and updated, and the whole WinDbg analysis session has been redone for Windows 10.
Reihe
Auflage
Sprache
Produkt-Hinweis
Broschur/Paperback
Klebebindung
Maße
Höhe: 280 mm
Breite: 216 mm
Dicke: 4 mm
Gewicht
ISBN-13
978-1-912636-80-8 (9781912636808)
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Schweitzer Klassifikation
Dmitry Vostokov is an internationally recognized expert, speaker, educator, scientist, inventor, and author. He founded the pattern-oriented software diagnostics, forensics, and prognostics discipline (Systematic Software Diagnostics) and Software Diagnostics and Observability Institute. Vostokov has also authored over 50 books on software diagnostics, anomaly detection and analysis, software and memory forensics, root cause analysis and problem solving, memory dump analysis, debugging, software trace and log analysis, reverse engineering, and malware analysis. He has over 30 years of experience in software architecture, design, development, and maintenance in various industries, including leadership, technical, and people management roles. Dmitry founded OpenTask Iterative and Incremental Publishing and Software Diagnostics Technology and Services (former Memory Dump Analysis Services). In his spare time, he explores Software Narratology and Quantum Software Diagnostics. His interest areas are theoretical software diagnostics and its mathematical and computer science foundations, application of formal logic, semiotics, artificial intelligence, machine learning, and data mining to diagnostics and anomaly detection, software diagnostics engineering and diagnostics-driven development, diagnostics workflow and interaction. Recent interest areas also include functional programming, cloud native computing, monitoring, observability, visualization, security, automation, applications of category theory to software diagnostics, development and big data, and diagnostics of artificial intelligence.
