Practical Forensic Analysis of Artifacts on iOS and Android Devices

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.

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.