Book Image

iOS Forensics for Investigators

By : Gianluca Tiepolo
5 (1)
Book Image

iOS Forensics for Investigators

5 (1)
By: Gianluca Tiepolo

Overview of this book

Professionals working in the mobile forensics industry will be able to put their knowledge to work with this practical guide to learning how to extract and analyze all available data from an iOS device. This book is a comprehensive, how-to guide that leads investigators through the process of collecting mobile devices and preserving, extracting, and analyzing data, as well as building a report. Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this book starts by covering the fundamentals of mobile forensics and how to overcome challenges in extracting data from iOS devices. Once you've walked through the basics of iOS, you’ll learn how to use commercial tools to extract and process data and manually search for artifacts stored in database files. Next, you'll find out the correct workflows for handling iOS devices and understand how to extract valuable information to track device usage. You’ll also get to grips with analyzing key artifacts, such as browser history, the pattern of life data, location data, and social network forensics. By the end of this book, you'll be able to establish a proper workflow for handling iOS devices, extracting all available data, and analyzing it to gather precious insights that can be reported as prosecutable evidence.
Table of Contents (17 chapters)
1
Section 1 – Data Acquisition from iOS Devices
4
Section 2 – iOS Data Analysis
14
Section 3 – Reporting

Using open source tools

This chapter would not be complete without mentioning some of the best open source iOS forensic tools! The DFIR (Digital Forensics and Incident Response) community is one of the most active and helpful communities out there, and these tools are the result of the hard work and passion of security researchers, forensic examiners, and developers that have shared their knowledge with the community.

Apollo

The Apple Pattern of Life Lazy Output'er (APOLLO) is a Python script written by Sarah Edwards (you can find her on Twitter – @iamevltwin) that processes iOS and macOS artifacts to extract pattern-of-life data and combines the result into a single SQLite database or CSV file for viewing.

The script is based on a number of different modules (some of these have been written by the DFIR community), and each of these runs one or more queries to extract specific data from an iOS extraction. We'll be using this powerful tool extensively in Chapter...