TY - GEN
T1 - A targeted data extraction system for mobile devices
AU - Aggarwal, Sudhir
AU - Dorai, Gokila
AU - Karabiyik, Umit
AU - Mukherjee, Tathagata
AU - Guerra, Nicholas
AU - Hernandez, Manuel
AU - Parsons, James
AU - Rathi, Khushboo
AU - Chi, Hongmei
AU - Aderibigbe, Temilola
AU - Wilson, Rodney
N1 - Funding Information:
iPhones: Apple iOS security mechanisms do not permit applications that execute on an iPhone to extract certain types of content (second and third sections of Table 1). Therefore, this content is acquired from an iTunes backup. The idevicebackup2 command supported by the open-source libimobiledevice [21] is employed. Other standard, albeit complex, techniques can also be used to extract data from a backup.
Publisher Copyright:
© IFIP International Federation for Information Processing 2019.
PY - 2019
Y1 - 2019
N2 - Smartphones contain large amounts of data that are of significant interest in forensic investigations. In many situations, a smartphone owner may be willing to provide a forensic investigator with access to data under a documented consent agreement. However, for privacy or personal reasons, not all the smartphone data may be extracted for analysis. Courts have also opined that only data relevant to the investigation at hand may be extracted. This chapter describes the design and implementation of a targeted data extraction system for mobile devices. It assumes user consent and implements state-of-the-art filtering using machine learning techniques. The system can be used to identify and extract selected data from smartphones in real time at crime scenes. Experiments conducted with iOS and Android devices demonstrate the utility of the targeted data extraction system.
AB - Smartphones contain large amounts of data that are of significant interest in forensic investigations. In many situations, a smartphone owner may be willing to provide a forensic investigator with access to data under a documented consent agreement. However, for privacy or personal reasons, not all the smartphone data may be extracted for analysis. Courts have also opined that only data relevant to the investigation at hand may be extracted. This chapter describes the design and implementation of a targeted data extraction system for mobile devices. It assumes user consent and implements state-of-the-art filtering using machine learning techniques. The system can be used to identify and extract selected data from smartphones in real time at crime scenes. Experiments conducted with iOS and Android devices demonstrate the utility of the targeted data extraction system.
KW - Android
KW - Mobile devices
KW - iOS
KW - privacy
KW - targeted data extraction
UR - http://www.scopus.com/inward/record.url?scp=85071424157&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071424157&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-28752-8_5
DO - 10.1007/978-3-030-28752-8_5
M3 - Conference contribution
AN - SCOPUS:85071424157
SN - 9783030287511
T3 - IFIP Advances in Information and Communication Technology
SP - 73
EP - 100
BT - Advances in Digital Forensics- 15th IFIP WG 11.9 International Conference, 2019, Revised Selected Papers
A2 - Peterson, Gilbert
A2 - Shenoi, Sujeet
PB - Springer New York LLC
T2 - 15th IFIP WG 11.9 International Conference on Digital Forensics, DigitalForensics 2019
Y2 - 28 January 2019 through 29 January 2019
ER -