Abstract
Standardization of digital forensics has become an important focus area for researchers and criminal justice practitioners. Over the past decade, several efforts have been made to encapsulate digital forensic processes and activities in harmonized frameworks for incident investigations. A harmonized model for digital evidence admissibility assessment has been proposed for integrating the technical and legal determinants of digital evidence admissibility, thereby providing a techno-legal foundation for assessing digital evidence admissibility in judicial proceedings.
This chapter presents an algorithm underlying the harmonized model for digital evidence admissibility assessment, which enables the determination of the evidential weight of digital evidence using factor analysis. The algorithm is designed to be used by judges to determine evidence admissibility in criminal proceedings. However, it should also be useful to investigators, prosecutors and defense lawyers for evaluating potential digital evidence before it is presented in court.
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References
S. Alabdulsalam, K. Schaefer, T. Kechadi and N. Le-Khac, Internet of Things forensics: Challenges and a case study, in Advances in Digital Forensics XIV, G. Peterson and S. Shenoi (Eds.), Springer, Cham, Switzerland, pp. 35–48, 2018.
A. Antwi-Boasiako and H. Venter, A model for digital evidence assessment, in Advances in Digital Forensics XIII, G. Peterson and S. Shenoi (Eds.), Springer, Cham, Switzerland, pp. 23–38, 2017.
Association of Chief Police Officers, Good Practice Guide for Computer-Based Evidence, London, United Kingdom, 2008.
D. Bertram, Likert Scales ...are the Meaning of Life, CPSC 681 – Topic Report (poincare.matf.bg.ac.rs/~kristina/topic-dane-likert.pdf), 2008.
A. Bryman and D. Cramer, Constructing variables, in Handbook of Data Analysis, M. Hardy and A. Bryman (Eds.), SAGE Publications, London, United Kingdom, pp. 18–34, 2004.
L. Burton and S. Mazerolle, Survey instrument validity, Part I: Principles of survey instrument development and validation in athletic training education research, Athletic Training Education Journal, vol. 6(1), pp. 27–35, 2011.
E. Casey, Digital Evidence and Computer Crime: Forensic Science, Computers and the Internet, Academic Press, Waltham, Massachusetts, 2011.
D. Child, The Essentials of Factor Analysis, Bloomsbury Academic, London, United Kingdom, 2006.
T. Cowper and B. Levin, Autonomous vehicles: How will they challenge law enforcement? Law Enforcement Bulletin, FBI Training Division, Federal Bureau of Investigation, Quantico, Virginia (leb.fbi.gov/articles/featured-articles/autonomous-vehicles-how-will-they-challenge-law-enforcement), February 13, 2018.
I. Etikan and K. Bala, Sampling and sampling methods, Biometrics and Biostatistics International Journal, vol. 5(6), article no. 00148, 2017.
K. Franke and S. Srihari, Computational forensics: An overview, Proceedings of the Second International Workshop on Computational Forensics, pp. 1–10, 2008.
S. Goodison, R. Davis and B. Jackson, Digital Evidence and the U.S. Criminal Justice System: Identifying Technology and Other Needs to More Effectively Acquire and Utilize Digital Evidence, Technical Report RR 890-NIJ, RAND Corporation, Santa Monica, California, 2015
International Organization for Standardization, Information Technology – Security Techniques – Guidelines for Identification, Collection, Acquisition and Preservation of Digital Evidence, ISO/IEC 27037:2012 Standard, Geneva, Switzerland, 2012.
International Organization for Standardization, Information Technology – Security Techniques – Incident Investigation Principles and Processes, ISO/IEC 27043:2015 Standard, Geneva, Switzerland, 2015.
S. Mason, Electronic Evidence, Butterworths Law, London, United Kingdom, 2012.
Organisation for Economic Co-operation and Development, Handbook on Constructing Composite Indicators: Methodology and User Guide, OECD Publishing, Paris, France, 2008.
G. Palmer, A Road Map for Digital Forensic Research, DFRWS Technical Report, DTR-T001-01 Final, Air Force Research Laboratory, Rome, New York, 2001.
M. Reith, C. Carr and G. Gunsch, An examination of digital forensic models, International Journal of Digital Evidence, vol. 1(3), 2002.
StataCorp, Stata Release 15, College Station, Texas (www.stata.com/products), 2019.
Statistics How To, Kaiser-Meyer-Olkin (KMO) Test for Sampling Adequacy (statisticshowto.com/kaiser-meyer-olkin), 2016.
Study.com, Pearson Correlation Coefficient: Formula, Example and Significance, Mountain View, California (study.com/academy/lesson/pearson-correlation-coefficient-formula-example-significance.html), 2019.
H. Taherdoost, Validity and reliability of the research instrument: How to test the validation of a questionnaire/survey in a research, International Journal of Academic Research in Management, vol. 5(3), pp. 28–36, 2016.
Technical Working Group for Electronic Crime Scene Investigation, Electronic Crime Scene Investigation: A Guide for First Responders, NIJ Guide, NCJ 187736, U.S. Department of Justice, Washington, DC, 2001.
R. Trotter, Qualitative research sample design and sample size: Resolving and unresolved issues and inferential imperatives, Preventive Medicine Journal, vol. 55(5), pp. 398–400, 2012.
A. Valjarevic and H. Venter, Harmonized digital forensic process model, Proceedings of the Information Security for South Africa Conference, 2012.
S. Weller and A. Romney, Systematic Data Collection, SAGE Publications, Newbury Park, California, 1988.
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Antwi-Boasiako, A., Venter, H. (2019). Implementing the Harmonized Model for Digital Evidence Admissibility Assessment. In: Peterson, G., Shenoi, S. (eds) Advances in Digital Forensics XV. DigitalForensics 2019. IFIP Advances in Information and Communication Technology, vol 569. Springer, Cham. https://doi.org/10.1007/978-3-030-28752-8_2
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