Document Type

Conference Proceeding

Conference

2018 ACM SIGSAC Conference on Computer and Communications

Publication Version

Accepted Manuscript

Link to Published Version

https://doi.org/10.1145/3243734.3243808

Publication Date

10-2018

First Page

1338

Last Page

1350

DOI

10.1145/3243734.3243808

Conference Date

October 15-19, 2018

City

Toronto, CA

Abstract

Crimes, both physical and cyber, increasingly involve smartphones due to their ubiquity. Therefore, digital evidence on smartphones plays an increasingly important role in crime investigations. Digital evidence could reside in the memory and permanent storage of a smartphone. While we have witnessed significant progresses on memory forensics recently, identifying evidence in the permanent storage is still an underdeveloped research area. Most existing studies on permanent-storage forensics rely on manual analysis or keyword-based scanning of the permanent storage. Manual analysis is costly, while keyword matching often misses the evidentiary data that do not have interesting keywords. In this work, we develop a tool called EviHunter to automatically identify evidentiary data in the permanent storage of an Android device. There could be thousands of files on the permanent storage of a smartphone. A basic question a forensic investigator often faces is which files could store evidentiary data. EviHunter aims to answer this question. Our intuition is that the evidentiary data were produced by apps; and an app's code has rich information about the types of data the app may write to a permanent storage and the files the data are written to. Therefore, EviHunter first pre-computes an App Evidence Database (AED) via static analysis of a large number of apps. The AED includes the 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 AED to identify the files that could store evidentiary data. We evaluate EviHunter on benchmark apps and 8,690 real-world apps. Our results show that EviHunter can precisely identify both the types of evidentiary data and the files that store them.

Comments

This is a manuscript of a proceeding published as Cheng, Chris Chao-Chun, Chen Shi, Neil Zhenqiang Gong, and Yong Guan. "EviHunter: identifying digital evidence in the permanent storage of android devices via static analysis." In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp. 1338-1350. 2018.

Copyright Owner

Association for Computing Machinery

Language

en

File Format

application/pdf

Published Version

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Article Location

 
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