Learning Python for Forensics Second Edition

▶Book Description
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.

▶What You Will Learn
⦁ Learn how to develop Python scripts to solve complex forensic problems
⦁ Build scripts using an iterative design
⦁ Design code to accommodate present and future hurdles
⦁ Leverage built-in and community-sourced libraries
⦁ Understand the best practices in forensic programming
⦁ Learn how to transform raw data into customized reports and visualizations
⦁ Create forensic frameworks to automate analysis of multiple forensic artifacts
⦁ Conduct effective and efficient investigations through programmatic processing

▶Key Features
⦁ Discover how to develop Python scripts for effective digital forensic analysis
⦁ Master the skills of parsing complex data structures with Python libraries
⦁ Solve forensic challenges through the development of practical Python scripts

▶Who This Book Is For
If you are a forensics student, hobbyist, or professional seeking to increase your understanding in forensics through the use of a programming language, then Learning Python for Forensics is for you.

You are not required to have previous experience in programming to learn and master the content within this book. This material, created by forensic professionals, was written with a unique perspective and understanding for examiners who wish to learn programming.

▶What this book covers
⦁ Chapter 1, Now for Something Completely Different, is an introduction to common Python objects, built-in functions, and tropes. We will also cover basic programming concepts.

⦁ Chapter 2, Python Fundamentals, is a continuation of the basics learned in the previous chapter and the development of our first forensic script.

⦁ Chapter 3, Parsing Text Files, discusses a basic setup API log parser to identify first use times for USB devices and introduce the iterative development cycle.

⦁ Chapter 4, Working with Serialized Data Structures, shows how serialized data structures such as JSON files can be used to store or retrieve data in Python. We will parse JSONformatted data from the Bitcoin blockchain containing transaction details.

⦁ Chapter 5, Databases in Python, shows how databases can be used to store and retrieve data via Python. We will use two different database modules to demonstrate different versions of a script that creates an active file listing with a database backend.

⦁ Chapter 6, Extracting Artifacts from Binary Files, is an introduction to the struct module, which will become every examiner's friend. We use the struct module to parse binary data into Python objects from a forensically-relevant source. We will parse the UserAssist key in the registry for user application execution artifacts.

⦁ Chapter 7, Fuzzy Hashing, explores how ssdeep compatible hashes are generated and how to use the pre-built ssdeep module to perform similarity analysis.

⦁ Chapter 8, The Media Age, helps us understand embedded metadata and parse them from forensic sources. In this chapter, we introduce and design an embedded metadata framework in Python.

⦁ Chapter 9, Uncovering Time, provides the first look at the development of the GUI with Python to decode commonly encountered timestamps. This is our introduction to GUI and Python class development.

⦁ Chapter 10, Rapidly Triage Systems, shows how you can use Python to collect volatile and other useful information from popular operating systems. This includes an introduction to a very powerful Windows-specific Python API.

⦁ Chapter 11, Parsing Outlook PST Containers, demonstrates how to read, index, and report on the contents of an Outlook PST container.

⦁ Chapter 12, Recovering Deleted Database Records, introduces SQLite Write-Ahead Logs and how to extract data, including deleted data, from these files.

⦁ Chapter 13, Coming Full Circle, is an aggregation of scripts written in previous chapters into a forensic framework. We explore concepts and methods for designing these larger projects.