Book Image

Learning Python for Forensics - Second Edition

By : Preston Miller, Chapin Bryce
Book Image

Learning Python for Forensics - Second Edition

By: Preston Miller, Chapin Bryce

Overview of this book

Digital forensics plays an integral role in solving complex cybercrimes and helping organizations make sense of cybersecurity incidents. This second edition of Learning Python for Forensics illustrates how Python can be used to support these digital investigations and permits the examiner to automate the parsing of forensic artifacts to spend more time examining actionable data. The second edition of Learning Python for Forensics will illustrate how to develop Python scripts using an iterative design. Further, it demonstrates how to leverage the various built-in and community-sourced forensics scripts and libraries available for Python today. This book will help strengthen your analysis skills and efficiency as you creatively solve real-world problems through instruction-based tutorials. By the end of this book, you will build a collection of Python scripts capable of investigating an array of forensic artifacts and master the skills of extracting metadata and parsing complex data structures into actionable reports. Most importantly, you will have developed a foundation upon which to build as you continue to learn Python and enhance your efficacy as an investigator.
Table of Contents (15 chapters)

SQLite WAL files

When analyzing SQLite databases, the examiner might come across additional temporary files. There are nine types of temporary SQLite files:

  • Rollback journals
  • Master journals
  • Statement journals
  • WAL
  • Shared-memory files
  • TEMP databases
  • Views and subqueries materializations
  • Transient indices
  • Transient databases

For more details on these files, refer to https://www.sqlite.org/tempfiles.html, which describes these files in greater detail. The WAL is one of these temporary files and is involved in the atomic commit and rollback scenarios. Only databases that have set their journaling mode to WAL will use the write ahead log method. The following SQLite command is required to configure a database to use WAL journaling:

PRAGMA journal_mode=WAL; 

The WAL file is created in the same directory as the SQLite database with -wal appended to the original SQLite database filename...