Book Image

Python Digital Forensics Cookbook

By : Chapin Bryce, Preston Miller
Book Image

Python Digital Forensics Cookbook

By: Chapin Bryce, Preston Miller

Overview of this book

Technology plays an increasingly large role in our daily lives and shows no sign of stopping. Now, more than ever, it is paramount that an investigator develops programming expertise to deal with increasingly large datasets. By leveraging the Python recipes explored throughout this book, we make the complex simple, quickly extracting relevant information from large datasets. You will explore, develop, and deploy Python code and libraries to provide meaningful results that can be immediately applied to your investigations. Throughout the Python Digital Forensics Cookbook, recipes include topics such as working with forensic evidence containers, parsing mobile and desktop operating system artifacts, extracting embedded metadata from documents and executables, and identifying indicators of compromise. You will also learn to integrate scripts with Application Program Interfaces (APIs) such as VirusTotal and PassiveTotal, and tools such as Axiom, Cellebrite, and EnCase. By the end of the book, you will have a sound understanding of Python and how you can use it to process artifacts in your investigations.
Table of Contents (11 chapters)

Using HTML templates

Recipe Difficulty: Easy

Python Version: 2.7 or 3.5

Operating System: Any

HTML can be an effective medium for a report. There are a great number of snazzy templates out there that can make even technical reports look appealing. That's the first step towards hooking the audience. Or, at the very least, a preventative measure to forestall the audience from instantly nodding off. This recipe uses one such template and some test data to create a visually compelling example of acquisition details. We really have our work cut out for us here.

Getting started

This recipe introduces HTML templating with the jinja2 module. The jinja2 library is a very powerful tool and has a number of different documented features...