This training course extends pattern-oriented analysis introduced in Accelerated Windows Memory Dump Analysis, Accelerated .NET Core Memory Dump Analysis, Advanced Windows Memory Dump Analysis with Data Structures, and Accelerated Windows Malware Analysis with Memory Dumps courses with elements of programming, data engineering, data science, machine learning, and AI:
- Surveying the current landscape of WinDbg extensions with analysis pattern mappings
- Writing WinDbg extensions in C, C++, Rust, and Python
- Connecting WinDbg to NoSQL databases
- Connecting WinDbg to streaming and log processing platforms
- Querying and visualizing WinDbg output data
- Using Data Science, Machine Learning, and AI for diagnostics and postmortem debugging
- Using Coding Assistants to automate analysis patterns and model complex problems (new)
- Using GenAI for memory analysis (new)
The new version of the training updates existing and includes entirely new exercises and analysis patterns.
Registration: TBD
Slides from the previous training
Before the training, you get:
- The current PDF book version and the previous recording of the training
- Practical Foundations of Windows Debugging, Disassembling, Reversing, Third Edition PDF book
- Access to Software Diagnostics Library
After the training, you also get:
- The new edition of the PDF book version of the training
- Personalized Certificate of Attendance with unique CID
- Answers to questions during training sessions
- New recording
Prerequisites: Working knowledge of WinDbg. Working knowledge of Python, C, C++, or Rust is optional (required only for some exercises). Other concepts are explained when necessary.
Audience: Software developers, software maintenance engineers, escalation engineers, quality assurance engineers, security and vulnerability researchers, malware and memory forensics analysts who want to build memory analysis pipelines.
If you are interested in ML/AI for Linux core dump analysis, there is another course available: Advanced Linux Core Dump Analysis with Data Structures, Machine Learning, and AI