Moreb Practical Forensic Analysis of Artifacts on iOS and Android Devices

Investigating Complex Mobile Devices
1. Auflage 2022
ISBN: 978-1-4842-8026-3
Verlag: APRESS
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 515 Seiten, eBook

Leverage foundational concepts and practical skills in mobile device forensics to perform forensically sound criminal investigations involving the most complex mobile devices currently available on the market. Using modern tools and techniques, this book shows you how to conduct a structured investigation process to determine the nature of the crime and to produce results that are useful in criminal proceedings.  You’ll walkthrough the various phases of the mobile forensics process for both Android and iOS-based devices, including forensically extracting, collecting, and analyzing data and producing and disseminating reports. Practical cases and labs involving specialized hardware and software illustrate practical application and performance of data acquisition (including deleted data) and the analysis of extracted information. You'll also gain an advanced understanding of computer forensics, focusing on mobile devices and other devices not classifiable as laptops, desktops, or servers.  This book is your pathway to developing the critical thinking, analytical reasoning, and technical writing skills necessary to effectively work in a junior-level digital forensic or cybersecurity analyst role.   What You'll Learn Acquire and investigate data from mobile devices using forensically sound, industry-standard tools Understand the relationship between mobile and desktop devices in criminal and corporate investigations Analyze backup files and artifacts for forensic evidenceWho This Book Is For Forensic examiners with little or basic experience in mobile forensics or open source solutions for mobile forensics. The book will also be useful to anyone seeking a deeper understanding of mobile internals.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Moreb, Mohammed

Inhaltsverzeichnis

Chapter 1: Introduction to Mobile Forensic Analysis.- Chapter 2: Introduction to IOS Forensics.- Chapter 3: Introduction to Android Forensics.- Chapter 4: Forensic Investigations of Popular Applications on Android and iOS platforms.- Chapter 5: Forensic Analysis of Telegram Messenger on iOS and Android Smartphones Case Study.- Chapter 6: Detecting Privacy Leaks Utilizing Digital Forensics and Reverse Engineering Methodologies.Chapter 7: Impact of Device Jailbreaking or Rooting on User Data Integrity in Mobile Forensics.- Chapter 8: The Impact of Cryptocurrency Mining on Mobile Devices.- Chapter 9: Mobile Forensic Investigation for WhatsApp.- Chapter 10: Cloud Computing Forensics: Dropbox Case Study.- Chapter 11: Malware Forensics for Volatile and nonVolatile Memory in Mobile Devices.- Chapter 12: Mobile Forensic for KeyLogger Artifact.- Chapter 13: Evidence Identification Methods for Android and iOS Mobile Devices with Facebook Messenger.- Chapter 14: Mobile Forensics for IOS and Android platforms: Chrome app artifacts depending on SQLite.

Über Autor(innen)

Mohammed Moreb, Ph.D. in Electrical and Computer Engineering. Expertise in Cybercrimes & Digital Evidence Analysis, specifically focusing on Information and Network Security, with a strong publication track record, work for both conceptual and practical wich built during works as a system developer and administrator for the data center for more than 10 years, config, install, and admin enterprise system related to all security configuration, he improved his academic path with the international certificate such as CCNA, MCAD, MCSE; Academically he teaches the graduate-level courses such as Information and Network Security course, Mobile Forensics course, Advanced Research Methods, Computer Network Analysis and Design, and Artificial Intelligence Strategy for Business Leaders. Dr. Moreb recently founded a new framework and methodology specialized in software engineering for machine learning in health informatics named SEMLHI which investigates the interaction between software engineering and machine learning within the context of health systems. The SEMLHI framework includes four modules (software, machine learning, machine learning algorithms, and health informatics data) that organize the tasks in the framework using a SEMLHI methodology, thereby enabling researchers and developers to analyze health informatics software from an engineering perspective and providing developers with a new road map for designing health applications with system functions and software implementations.