A major advancement in vault apps detection could transform digital forensics. Researchers in Australia have developed a machine learning model that can identify hidden apps on Android phones with up to 98% accuracy—without prior knowledge of the app.
Breakthrough in Smartphone Forensics
The study, led by Edith Cowan University and the University of Southern Queensland, targets vault apps—applications that hide files, texts, or even other apps behind encryption. These apps often disguise themselves as normal tools, making them difficult to trace.
Traditional Methods vs AI
Current detection techniques rely on predefined app databases. In conclusion This limits investigators, as new or disguised apps go unnoticed. However, the new machine learning technique doesn’t need such a list. It learns patterns, allowing it to flag hidden apps even if they’ve never been seen before.
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Associate Professor Mike Johnstone from Edith Cowan University explained, “These apps are designed to mimic regular ones, which poses a big challenge. Our AI model sidesteps this issue entirely.”
Boost for Law Enforcement
With over 5 billion smartphone users worldwide, non-invasive and accurate tools for digital searches are becoming essential. Vault apps have gained notoriety for their use in espionage, unauthorized surveillance, and hiding criminal activity.
“This could be a game-changer,” added Professor Johnstone. “The ability to detect hidden apps without tipping off users or invading privacy excessively is a major leap forward.”
Broader Plans Ahead
The research team plans to scale the system. However Future work will include testing more algorithms, using larger datasets, and applying the method to devices beyond Android.
This innovation could soon become a staple in digital forensics, giving law enforcement a powerful edge while respecting user privacy boundaries. As smartphones continue to dominate daily life, staying ahead of covert misuse remains a top priority.
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