Using Machine Learning to Detect Vault (Anti-Forensic) Apps

Article

Johnstone, Michael N., Yang, Wencheng and Ahmad, Mohiuddin. 2025. "Using Machine Learning to Detect Vault (Anti-Forensic) Apps." Future Internet. 17 (5). https://doi.org/10.3390/fi17050186
Article Title

Using Machine Learning to Detect Vault (Anti-Forensic) Apps

ERA Journal ID212586
Article CategoryArticle
AuthorsJohnstone, Michael N., Yang, Wencheng and Ahmad, Mohiuddin
Journal TitleFuture Internet
Journal Citation17 (5)
Article Number186
Number of Pages15
Year2025
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN1999-5903
Digital Object Identifier (DOI)https://doi.org/10.3390/fi17050186
Web Address (URL)https://www.mdpi.com/1999-5903/17/5/186
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.

KeywordsAndroid; vault apps; software development; machine learning; malware detection; content hiding
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460299. Artificial intelligence not elsewhere classified
Byline AffiliationsEdith Cowan University
School of Science, Engineering & Digital Technologies- Maths,Physics & Computing
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