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Crimes, both physical and cyber, increasingly involve smartphones due totheir ubiquity. Therefore, digital evidence on smartphones plays anincreasingly important role in crime investigations. Digital evidence couldreside in the memory and permanent storage of a smartphone. While we havewitnessed significant progresses on memory forensics recently, identifyingevidence in the permanent storage is still an underdeveloped research area.Most existing studies on permanent-storage forensics rely on manual analysis orkeyword-based scanning of the permanent storage. Manual analysis is costly,while keyword matching often misses the evidentiary data that do not haveinteresting keywords.  In this work, we develop a tool called EviHunter to automatically identifyevidentiary data in the permanent storage of an Android device. There could bethousands of files on the permanent storage of a smartphone. A basic question aforensic investigator often faces is which files could store evidentiary data.EviHunter aims to answer this question. Our intuition is that the evidentiarydata were produced by apps; and an app's code has rich information about thetypes of data the app may write to a permanent storage and the files the dataare written to. Therefore, EviHunter first pre-computes an App EvidenceDatabase (AED) via static analysis of a large number of apps. The AED includesthe types of evidentiary data and files that store them for each app. Then,EviHunter matches the files on a smartphone's permanent storage against the AEDto identify the files that could store evidentiary data.  We evaluate EviHunter on benchmark apps and 8,690 real-world apps. Ourresults show that EviHunter can precisely identify both the types ofevidentiary data and the files that store them.

EviHunter: Identifying Digital Evidence in the Permanent Storage of Android Devices via Static Analysis