Performing forensic analysis on mobile devices necessitates a suite of specialized tools to ensure that data acquisition, analysis, and reporting are conducted in a forensically sound manner. In this post I’ll try to delve into the technical aspects of several essential tools used in mobile forensics, providing expert analysts with a detailed understanding of their functionalities, applications, and methodologies.

Overview of Mobile Forensics

Mobile forensics is a branch of digital forensics focused on the recovery of digital evidence from mobile devices under forensically sound conditions. This process involves several critical steps, including:

  1. Acquisition: The process of creating a bit-by-bit copy of the mobile device’s storage media.
  2. Examination: Analyzing the acquired data to uncover relevant information.
  3. Analysis: Interpreting the data to understand its context and significance.
  4. Reporting: Documenting the findings in a clear and concise manner for legal proceedings.

Challenges in Mobile Forensics

  • Diverse Operating Systems: Different platforms like Android, iOS (Windows?) require distinct forensic approaches.
  • Encryption: Modern mobile devices often employ robust encryption methods.
  • Data Volume: The sheer amount of data can be overwhelming.
  • Constant Updates: Frequent OS updates necessitate continuous learning and adaptation.

Essential Mobile Forensics Tools


Andriller is a versatile software utility that comprises a collection of forensic tools designed for smartphones. It focuses on read-only, forensically sound, and non-destructive data acquisition from Android devices.

Key Features:

  • Data Acquisition: Supports various acquisition methods including ADB backup, full physical dump, and logical extraction.
  • Data Parsing: Extracts and decodes data such as contacts, call logs, SMS, browser history, and application data.
  • SQLite Viewer: Built-in viewer for analyzing SQLite databases found on mobile devices.

Use Case:

  • Scenario: Extracting chat history from a suspect’s Android device.
  • Method: Utilize Andriller to perform a logical extraction and parse the data to retrieve messages from popular chat applications.

ALEAPP (Android Logs Events and Protobuf Parser)

ALEAPP is an open-source tool designed to parse various log files, events, and protobuf data from Android devices. It is essential for uncovering application-specific data and system logs.

Key Features:

  • Wide Range of Parsers: Supports parsing data from numerous Android apps and system components.
  • Customizable Reports: Generates detailed reports in HTML format, making data interpretation easier.
  • Regular Updates: Continuously updated to support new apps and Android versions.

Use Case:

  • Scenario: Investigating a suspect’s activity through app usage.
  • Method: Use ALEAPP to parse log files and protobuf data, providing insights into app interactions, timestamps, and user behavior.

iOS Frequent Locations Dumper

This tool is specifically designed to dump the contents of the StateModel#.archive files located in /private/var/mobile/Library/Caches/ on iOS devices. These files contain data on frequently visited locations, which can be crucial in tracking a suspect’s movements.

Key Features:

  • Location Data Extraction: Extracts and decodes location data from iOS devices.
  • Time-Based Analysis: Provides timestamps for each recorded location, enabling detailed movement tracking.
  • Data Visualization: Capable of exporting data to formats suitable for geographical plotting.

Use Case:

  • Scenario: Tracking the movements of a suspect over a period.
  • Method: Employ the tool to extract frequent location data and visualize the movement patterns to identify significant locations.

MEAT (Mobile Evidence Acquisition Tool)

MEAT is a comprehensive tool for performing various types of acquisitions on iOS devices. It supports logical, file system, and physical acquisitions, making it a versatile tool for iOS forensics.

Key Features:

  • Multi-Type Acquisitions: Supports different acquisition types to cater to various forensic needs.
  • Encrypted Data Handling: Capable of dealing with encrypted iOS backups.
  • Automation: Scripts and automation capabilities for repetitive tasks.

Use Case:

  • Scenario: Acquiring data from an iOS device that has been reset to factory settings.
  • Method: Use MEAT to perform a file system acquisition, extracting remnants of data and logs that might not be immediately visible.

MobSF (Mobile Security Framework)

MobSF is an automated, all-in-one mobile application (Android/iOS/Windows) pen-testing, malware analysis, and security assessment framework. It is adept at performing both static and dynamic analysis.

Key Features:

  • Static Analysis: Decompiles and analyzes APKs, IPAs, and other mobile application binaries.
  • Dynamic Analysis: Executes applications in a sandbox environment to monitor behavior.
  • Comprehensive Reporting: Provides detailed vulnerability reports and security assessments.

Use Case:

  • Scenario: Assessing a suspect’s mobile app for potential malware.
  • Method: Utilize MobSF to perform both static and dynamic analysis on the app, identifying any malicious code or behavior.


OpenBackupExtractor is an application designed for extracting data from iPhone and iPad backups. It supports encrypted backups and is invaluable for accessing data from backups created by iTunes or iCloud.

Key Features:

  • Encrypted Backup Support: Handles encrypted iOS backups, providing access to protected data.
  • Wide Data Range: Extracts a variety of data including contacts, messages, call logs, and app data.
  • Cross-Platform: Available for both Windows and macOS, enhancing usability across different forensic setups.

Use Case:

  • Scenario: Recovering data from a suspect’s iCloud backup.
  • Method: Use OpenBackupExtractor to access the iCloud backup, extracting pertinent data such as messages, photos, and application data for forensic analysis.

Advanced Tools and Techniques in Mobile Forensics

JTAG and Chip-Off Techniques

For situations where standard acquisition methods are ineffective, techniques like JTAG (Joint Test Action Group) and Chip-Off come into play.


  • Overview: Involves connecting to the device’s processor to access the memory directly.
  • Use Case: Useful for acquiring data from devices with damaged storage or inaccessible through software means.


  • Overview: Involves physically removing the memory chip from the device and reading it directly.
  • Use Case: Effective for devices with severe physical damage or advanced encryption that cannot be bypassed through logical means.

Cloud Forensics

With the increasing integration of cloud services, cloud forensics has become a crucial aspect of mobile forensics. It involves acquiring data stored in cloud services such as Google Drive, iCloud, and OneDrive.

Key Techniques:

  • API-Based Acquisition: Using official APIs to access and download user data.
  • Token-Based Access: Leveraging authentication tokens obtained from the device to access cloud data.


  • Legal and Jurisdictional Issues: Different countries have varying laws regarding access to cloud data.
  • Data Volatility: Cloud data can be altered or deleted quickly, necessitating rapid acquisition.

Best Practices for Mobile Forensics

  1. Forensically Sound Acquisition: Always ensure data is acquired in a manner that preserves its integrity and authenticity.
  2. Chain of Custody: Maintain a detailed chain of custody to document who has handled the evidence.
  3. Regular Tool Updates: Keep forensic tools updated to handle the latest device models and OS versions.
  4. Comprehensive Documentation: Document every step of the forensic process to provide transparency and support in legal proceedings.
  5. Training and Certification: Continuously train and certify in the latest forensic techniques and tools to stay proficient.