TY - JOUR
T1 - Systematic Literature Review on Wearable Digital Forensics
T2 - Acquisition Methods, Analysis Techniques, Tools, and Future Directions
AU - Almubairik, Norah Ahmed
AU - Alam Khan, Fakhri
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2025
Y1 - 2025
N2 - Wearable devices have become increasingly prevalent in workplaces worldwide, offering valuable information and forensic data to dispute false testimonies or track a victim during an incident. However, the use of wearables as sources of digital evidence remains relatively unexplored. Further, there has been no systematic review of data extraction and analysis techniques for wearables. This systematic literature review (SLR) addresses these gaps by: 1) exploring methods used by digital investigators to extract data from wearables; 2) surveying prevalent data analysis techniques for wearable digital forensics; 3) examining digital forensics tools used in wearable investigations; 4) proposing a taxonomy integrating data extraction methods, analysis techniques, and forensic tools; and 5) identifying gaps in current wearable forensics research to guide future studies. The SLR covered articles published in the last decade (2012-2022) on the extraction and analysis of evidence from wearables. Consequently, 50 primary studies relevant to the study's objectives were identified. Five main extraction techniques were identified: 1) manual; 2) logical; 3) physical; 4) network communication; and 5) electromagnetic. Logical data extraction accounted for approximately 48% of these methods, followed by physical extraction (31%). Notably, 47% employed multiple extraction techniques. Trivial, nontrivial, and anti-forensic techniques were the most commonly used by criminals to evade forensic investigations. Moreover, most tools examined for wearable investigations were from nonwearable domains. The review highlighted several research gaps that require future investigation to develop more sustainable approaches to wearable digital forensics. This comprehensive overview highlighted the need for advancing forensic methodologies to address the unique challenges posed by the wearable technology.
AB - Wearable devices have become increasingly prevalent in workplaces worldwide, offering valuable information and forensic data to dispute false testimonies or track a victim during an incident. However, the use of wearables as sources of digital evidence remains relatively unexplored. Further, there has been no systematic review of data extraction and analysis techniques for wearables. This systematic literature review (SLR) addresses these gaps by: 1) exploring methods used by digital investigators to extract data from wearables; 2) surveying prevalent data analysis techniques for wearable digital forensics; 3) examining digital forensics tools used in wearable investigations; 4) proposing a taxonomy integrating data extraction methods, analysis techniques, and forensic tools; and 5) identifying gaps in current wearable forensics research to guide future studies. The SLR covered articles published in the last decade (2012-2022) on the extraction and analysis of evidence from wearables. Consequently, 50 primary studies relevant to the study's objectives were identified. Five main extraction techniques were identified: 1) manual; 2) logical; 3) physical; 4) network communication; and 5) electromagnetic. Logical data extraction accounted for approximately 48% of these methods, followed by physical extraction (31%). Notably, 47% employed multiple extraction techniques. Trivial, nontrivial, and anti-forensic techniques were the most commonly used by criminals to evade forensic investigations. Moreover, most tools examined for wearable investigations were from nonwearable domains. The review highlighted several research gaps that require future investigation to develop more sustainable approaches to wearable digital forensics. This comprehensive overview highlighted the need for advancing forensic methodologies to address the unique challenges posed by the wearable technology.
KW - Acquisition
KW - analysis
KW - digital evidence
KW - digital forensics tools
KW - extraction
KW - Internet of Things (IoT)
KW - wearable digital forensics
UR - https://www.scopus.com/pages/publications/85207439592
U2 - 10.1109/JIOT.2024.3485027
DO - 10.1109/JIOT.2024.3485027
M3 - Article
AN - SCOPUS:85207439592
SN - 2327-4662
VL - 12
SP - 1320
EP - 1342
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 2
ER -