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)

Designing our script

The first iteration of our script focuses on performing the task at hand with a standard module, sqlite3, in a more manual fashion. This entails writing out each SQL statement and executing them as if you were working with the database itself. Although this is not a very Pythonic manner of handling a database, it demonstrates the methods that are used to interact with a database with Python. Our second iteration employs two third-party libraries: peewee and jinja2.

Peewee is an object-relational mapper (ORM), which is a term that's used to describe a software suite that uses objects to handle database operations. In short, this ORM allows the developer to call functions and define classes in Python that are interpreted as database commands. This layer of abstraction helps to standardize database calls and allows for multiple database backends to be easily...