Practical Forensic Analysis of Artifacts on iOS and Android Devices
Description
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
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Acquire and investigate data from mobile devices using forensically sound, industry-standard tools
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Understand the relationship between mobile and desktop devices in criminal and corporate investigations
- Analyze backup files and artifacts for forensic evidence
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.
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Person
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.