Abstract
Content hiding, or vault applications (apps), are designed with a secondary, often concealed purpose, such as encrypting and storing files. While these apps may serve legitimate functions, they unequivocally present significant challenges for law enforcement. Conventional methods for tackling this issue, whether static or dynamic, prove inadequate when devices—typically smartphones—cannot be modified. Additionally, these methods frequently require prior knowledge of which apps are classified as vault apps. This research decisively demonstrates that a non-invasive method of app analysis, combined with machine learning, can effectively identify vault apps. Our findings reveal that it is entirely possible to detect an Android vault app with 98% accuracy using a random forest classifier. This clearly indicates that our approach can be instrumental for law enforcement in their efforts to address this critical issue.
RAS ID
78838
Document Type
Journal Article
Date of Publication
5-1-2025
Volume
17
Issue
5
School
School of Science
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Publisher
MDPI
Identifier
Michael N. Johnstone: https://orcid.org/0000-0001-7192-7098
Mohiuddin Ahmed: https://orcid.org/0000-0002-4559-4768
Recommended Citation
Johnstone, M. N., Yang, W., & Ahmed, M. (2025). Using machine learning to detect vault (anti-forensic) apps. DOI: https://doi.org/10.3390/fi17050186
Comments
Johnstone, M. N., Yang, W., & Ahmed, M. (2025). Using machine learning to detect vault (anti-forensic) apps. Future Internet, 17(5). https://doi.org/10.3390/fi17050186