Innovative Learning in a Digital Forensics Laboratory: Tools and Techniques for Data Recovery

Electronic evidence is an essential component in most legal trials of criminal activities, and digital forensics is therefore a crucial support for law enforcement investigations. For instance, a wide range of electronic devices contain Not AND (NAND) flash memory chips, and when a criminal leaves digital evidence on non-operational or locked systems, accessing this memory is crucial. Student acquisition of the necessary competences and skills associated with electronic devices, their basic principles, and the associated technologies can be provided by experimental training, as done with the optional Digital Forensics module included in the degree in Criminalistics: Forensic Sciences and Technologies offered by the University of Alcalá (Spain). This module equips students with the appropriate skills to extract, process, and authenticate evidence information using suitable tools. The purpose of this study was to investigate the effectiveness of experimental learning, deployed through laboratory digital forensic tasks. A literature review was conducted of novel data extraction and analysis tools and procedures as a guide to the design of data recovery tasks incorporating experimental learning. Drawing on student feedback, our results highlight positive learning outcomes for the students. It is concluded that powerful forensic image analysis freeware is capable of identifying elements, and practical tests involving JTAG/chip−off extraction and analysis yield favorable results. A proposal for future studies is to reduce the destructiveness of invasive extraction methods.

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Publication Details

Journal
Applied Sciences
Published
2024-11-28
DOI
https://doi.org/10.3390/app142311095
Citations
2
Primary Topic
Digital and Cyber Forensics
Type
article
Field-Weighted Citation Impact
1.56
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article

Innovative Learning in a Digital Forensics Laboratory: Tools and Techniques for Data Recovery

Carlos Cruz
2 citations
Applied Sciences
Digital and Cyber Forensics
1.56
article

Innovative Learning in a Digital Forensics Laboratory: Tools and Techniques for Data Recovery

Carlos Cruz
article en
2 citations

Abstract

Electronic evidence is an essential component in most legal trials of criminal activities, and digital forensics is therefore a crucial support for law enforcement investigations. For instance, a wide range of electronic devices contain Not AND (NAND) flash memory chips, and when a criminal leaves digital evidence on non-operational or locked systems, accessing this memory is crucial. Student acquisition of the necessary competences and skills associated with electronic devices, their basic principles, and the associated technologies can be provided by experimental training, as done with the optional Digital Forensics module included in the degree in Criminalistics: Forensic Sciences and Technologies offered by the University of Alcalá (Spain). This module equips students with the appropriate skills to extract, process, and authenticate evidence information using suitable tools. The purpose of this study was to investigate the effectiveness of experimental learning, deployed through laboratory digital forensic tasks. A literature review was conducted of novel data extraction and analysis tools and procedures as a guide to the design of data recovery tasks incorporating experimental learning. Drawing on student feedback, our results highlight positive learning outcomes for the students. It is concluded that powerful forensic image analysis freeware is capable of identifying elements, and practical tests involving JTAG/chip−off extraction and analysis yield favorable results. A proposal for future studies is to reduce the destructiveness of invasive extraction methods.

Applied SciencesVol. 14(23)
Universidad de Alcalá (ES)
Peace, Justice and strong institutions
Openalex Percentile: Top 12%
Digital and Cyber Forensics
1.56
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Innovative Learning in a Digital Forensics Laboratory: Tools and Techniques for Data Recovery — Carlos Cruz · Applied Sciences (2024) | TGRS Research Map | TGRS