MoLIFE: Methodology, Technologies, and Challenges for Mobile Live Intelligent Forensics Examination
Silvia Lucia Sanna, Cristina Alcaraz, Alessandro Sanna, Giorgio Giacinto, Javier Lopez
cs.CR
Submitted: 2026-07-08
Code: https://github.com/Nightbringer21/fridump
License: http://creativecommons.org/licenses/by/4.0/
The gist: Nowadays, mobile forensics is less explored in Digital Forensics case analysis due to the increase in data protection mechanisms implemented by tech companies (i.e., Google for Android and Apple for
Terminology
Abstract
Nowadays, mobile forensics is less explored in Digital Forensics case analysis due to the increase in data protection mechanisms implemented by tech companies (i.e., Google for Android and Apple for iOS). For example, the physical acquisition or analysis of specific directories under super-user protection would corrupt the evidence; access to such data is protected, and bypassing this protection requires either privilege escalation or custom ROM installation, leading to the modification of the device state. At the same time, the demand for mobile technologies and their respective communication systems is increasing exponentially, exposing numerous security threats and risks. For that reason, this paper presents a Mobile Live Intelligent Forensics Examination (MoLIFE), a novel Digital Forensics (DF) methodology for data acquisition and analysis of mobile devices. The proposed methodology is based on NIST SP800-101 for the DF process. MoLIFE can be integrated with new and emerging technologies by exploiting their power (e.g., AI, blockchain, quantum computing). MoLIFE can also be used to prevent cyber threats and incidents, as well as DF post-mortem analysis, offering examples of applying the MoLIFE methodology and good practices for the future. To prove the technical feasibility of the methodology, a small case study on Android devices data acquisition via the mDT will be presented. As the methodology is based on new and emerging technologies, it depends on their limitations that would be overcome in a few years.
Sources
- Responding to Living-Off-the-Land Tactics using Just-in-Time Memory Forensics (JIT-MF) for Android
- Systematic Evaluation of Forensic Data Acquisition using Smartphone Local Backup
- Exploring the Robustness of AI-Driven Tools in Digital Forensics: A Preliminary Study
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