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)

Uncovering Time

Timestamps are stored in a wide variety of formats unique to the operating system or application responsible for their generation. In forensics, converting these timestamps can be an important aspect of an investigation.

As an example, we may aggregate converted timestamps and create a combined timeline of events to determine a sequence of actions across mediums. This evaluation of time can help us establish whether actions are within a defined scope and provide insights into the relationship between two events.

To decipher these formatted timestamps, we can use tools to interpret the raw values and convert them into human-readable time. Most forensic tools perform this operation silently as they parse known artifact structures (similarly to how our scripts often parse Unix timestamps).

In some cases, we don't have tools that properly or uniformly handle specific...