Investigations of complex crimes with digital evidence increasingly require the use of modern digital devices and computer programs. Working with big data involves the accumulation, processing, and analysis of forensic information for further algorithmization and modeling of investigative actions, as well as the automation of the organizational activities of investigators. The article substantiates the need for the use of digital forensic logistics to optimize information flows and build the most effective analytical human and computer processing, not excluding the use of artificial intelligence systems. Digital forensic logistics is a sub-branch of digital forensics in the collection, identification, storage, verification, and analysis of data, as well as the generation of electronic evidence for evidence in court. The article provides the main directions of digital forensic logistics, including the logistics of evidence in criminal cases; logistics of the general organization of crime investigation; logistics planning (selection of tools and methods of investigation); logistics of putting forward versions of events; logistics of decisions in criminal matters. It is argued that the efficiency of the entire system will largely depend on the establishment of information flows and the prioritization of tasks. Quality work requires the improvement of applied digital technologies capable of providing the necessary algorithms of the evidentiary process. The use of special software, including the use of artificial intelligence systems, is becoming increasingly relevant. The logistics of making decisions in criminal cases ideally represents an electronic assistant, endowed with artificial intelligence or in the form of a special computer program, capable, based on the determination of the forensic significance of the obtained digital information (electronic evidence), to offer the investigator solutions that can change the course of the investigation and transfer the entire information system in a new state.
Published in | International Journal of Law and Society (Volume 4, Issue 2) |
DOI | 10.11648/j.ijls.20210402.14 |
Page(s) | 83-88 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2021. Published by Science Publishing Group |
Digital Forensics, Logistics, Algorithmization, Modeling, Big Data, Scientific Theory, Investigation
[1] | Walder H., Hansjakob T. (2016). Kriminalistisches Denken. Heidelberg, 2016. pp. 7–8. https://www.schulthess.com/buchshop/detail/ISBN-9783783200430/ Hansjakob-Thomas-Hrsg.-Walder-Hans/Kriminalistisches-Denken. |
[2] | Alesinskaya T. V. (2010). Fundamentals of Logistics. Functional areas of logistics management. Part 3. Taganrog: Publishing house of TTI SFU, 2010.116 p. |
[3] | Stelly Ch., Roussev V. (2018). Nugget: A digital forensics language // Digital Investigation. Vol. 24, Supplement, pp. 38-47. https://doi.org/10.1016/j.diin.2018.01.006. Khan Y., Varma S. (2020). Development and Design Strategies of Evidence Collection Framework in Cloud Environment. Social Networking and Computational Intelligence. Lecture Notes in Networks and Systems. Vol 100. Springer, pp. 27-37. https://www.springer.com/gp/book/9789811520709. |
[4] | Taha K., Yoo P. D. (2018). A Forensic System for Identifying the Suspects of a Crime with No Solid Material Evidences. 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing; 16th Intl Conf on Pervasive Intelligence and Computing; 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), Athens, 2018. рр. 576-583. https://ieeexplore.ieee.org/document/8511950. published 29 October 2018. |
[5] | Islam J., Anslow C., Xu K. and William Wong B. L. (2017). Analytical Provenance for Criminal Intelligence Analysis. Valcri white paper series. VALCRI–WP–2017–009 / ed. by B. L. William Wong. http://valcri.org/our-content/uploads/2017/02/VALCRI-WP-2017-009-Provenance3.pdf. |
[6] | Hilbe J. M. (2009). Logistic Regression Models, Chapman & Hall/CRC Press, (2009). https://books.google.ru/books/about/Logistic_Regression_Models.html?id= tmHMBQAAQBAJ&redir_esc=y. (Accessed 11 мая 2009). |
[7] | Gupta J. N. D., Kalaimannan E., Yoo SM. (2019). A Sequential Investigation Model for Solving Time Critical Digital Forensic Cases Involving a Single Investigator. National Cyber Summit (NCS) Research Track. NCS 2019. Advances in Intelligent Systems and Computing. Vol 1055. Springer, Cham, pp. 202-219. https://www.springer.com/gp/book/9783030312381. |
[8] | Bonomi S., Casini M., and Ciccotelli C. (2019). B-CoC: A Blockchain-Based Chain of Custody for Evidences Management in Digital Forensics. International Conference on Blockchain Economics, Security and Protocols, Tokenomics 2019, May 6–7, 2019. Paris, France. Article. No. 12, pp. 12: 1–12: 15. https://drops.dagstuhl.de/opus/volltexte/2020/11964/pdf/OASIcs-Tokenomics-2019-0.pdf. |
[9] | Sunde, N., Dror, I. E. (2019). Cognitive and human factors in digital forensics: Problems, challenges, and the way forward // Digital Investigation. 2019. 29, с. 101-108. https://www.sciencedirect.com/science/article/pii/S1742287619300441. |
[10] | Rassin E. 2018. Reducing tunnel vision with a pen-and-paper tool for the weighting of criminal evidence // Investigative Psychol. Vol. 15 (2) (2018), pp. 227-233. https://onlinelibrary.wiley.com/doi/abs/10.1002/jip.1504. published 14 April 2018. |
[11] | Liu, A., Liu, J., Uehara, T. (2014). Secure streaming forensic data transmission for trusted cloud SFCS 2014. Proceedings of the 2nd International Workshop on Security and Forensics in Communication Systems, pp. 3-10. https://www.scimagojr.com/journalsearch.php?q=21100320410&tip=sid&clean=0. |
[12] | Quinlan, J. R. (1986). Induction of decision trees. Mach Learn 1, pp. 81–106 (1986). https://link.springer.com/article/10.1007/BF00116251. published March 1986. |
[13] | Hoanrsm G. (2019). Formalising investigative decision making in digital forensics: Proposing the Digital Evidence Reporting and Decision Support (DERDS) framework. Digital Investigation. 2019. Vol. 28, pp. 146-151. https://research.tees.ac.uk/en/publications/formalising-investigative-decision-making-in-digital-forensics-pr. (Accessed 25 January 2019) published 1 March 2019. |
[14] | Olinder, N., Tsvetkov, A., Fedyakin, K., & Zaburdaeva, K. (2021). Using Digital Footprints in Social Research: an Interdisciplinary Approach. WISDOM, 16 (3), 124-135. https://doi.org/10.24234/wisdom.v16i3.403 |
[15] | Khan Y., Varma S. (2020). Development and Design Strategies of Evidence Collection Framework in Cloud Environment. Social Networking and Computational Intelligence. Lecture Notes in Networks and Systems. Vol 100. Springer, pp. 27-37. https://www.springer.com/gp/book/9789811520709. |
[16] | Soltani, S., Seno, S. A. H. (2019). A formal model for event reconstruction in digital forensic investigation Digital Investigation // Digital Investigation. 2019. 30, с. 148-160. https://www.sciencedirect.com/science/article/pii/S174228761930 1185. (Accessed 13 August 2019). |
APA Style
Sergey Zuev, Dmitry Bakhteev. (2021). Digital Forensic Logistics: The Basics of Scientific Theory. International Journal of Law and Society, 4(2), 83-88. https://doi.org/10.11648/j.ijls.20210402.14
ACS Style
Sergey Zuev; Dmitry Bakhteev. Digital Forensic Logistics: The Basics of Scientific Theory. Int. J. Law Soc. 2021, 4(2), 83-88. doi: 10.11648/j.ijls.20210402.14
AMA Style
Sergey Zuev, Dmitry Bakhteev. Digital Forensic Logistics: The Basics of Scientific Theory. Int J Law Soc. 2021;4(2):83-88. doi: 10.11648/j.ijls.20210402.14
@article{10.11648/j.ijls.20210402.14, author = {Sergey Zuev and Dmitry Bakhteev}, title = {Digital Forensic Logistics: The Basics of Scientific Theory}, journal = {International Journal of Law and Society}, volume = {4}, number = {2}, pages = {83-88}, doi = {10.11648/j.ijls.20210402.14}, url = {https://doi.org/10.11648/j.ijls.20210402.14}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijls.20210402.14}, abstract = {Investigations of complex crimes with digital evidence increasingly require the use of modern digital devices and computer programs. Working with big data involves the accumulation, processing, and analysis of forensic information for further algorithmization and modeling of investigative actions, as well as the automation of the organizational activities of investigators. The article substantiates the need for the use of digital forensic logistics to optimize information flows and build the most effective analytical human and computer processing, not excluding the use of artificial intelligence systems. Digital forensic logistics is a sub-branch of digital forensics in the collection, identification, storage, verification, and analysis of data, as well as the generation of electronic evidence for evidence in court. The article provides the main directions of digital forensic logistics, including the logistics of evidence in criminal cases; logistics of the general organization of crime investigation; logistics planning (selection of tools and methods of investigation); logistics of putting forward versions of events; logistics of decisions in criminal matters. It is argued that the efficiency of the entire system will largely depend on the establishment of information flows and the prioritization of tasks. Quality work requires the improvement of applied digital technologies capable of providing the necessary algorithms of the evidentiary process. The use of special software, including the use of artificial intelligence systems, is becoming increasingly relevant. The logistics of making decisions in criminal cases ideally represents an electronic assistant, endowed with artificial intelligence or in the form of a special computer program, capable, based on the determination of the forensic significance of the obtained digital information (electronic evidence), to offer the investigator solutions that can change the course of the investigation and transfer the entire information system in a new state.}, year = {2021} }
TY - JOUR T1 - Digital Forensic Logistics: The Basics of Scientific Theory AU - Sergey Zuev AU - Dmitry Bakhteev Y1 - 2021/04/26 PY - 2021 N1 - https://doi.org/10.11648/j.ijls.20210402.14 DO - 10.11648/j.ijls.20210402.14 T2 - International Journal of Law and Society JF - International Journal of Law and Society JO - International Journal of Law and Society SP - 83 EP - 88 PB - Science Publishing Group SN - 2640-1908 UR - https://doi.org/10.11648/j.ijls.20210402.14 AB - Investigations of complex crimes with digital evidence increasingly require the use of modern digital devices and computer programs. Working with big data involves the accumulation, processing, and analysis of forensic information for further algorithmization and modeling of investigative actions, as well as the automation of the organizational activities of investigators. The article substantiates the need for the use of digital forensic logistics to optimize information flows and build the most effective analytical human and computer processing, not excluding the use of artificial intelligence systems. Digital forensic logistics is a sub-branch of digital forensics in the collection, identification, storage, verification, and analysis of data, as well as the generation of electronic evidence for evidence in court. The article provides the main directions of digital forensic logistics, including the logistics of evidence in criminal cases; logistics of the general organization of crime investigation; logistics planning (selection of tools and methods of investigation); logistics of putting forward versions of events; logistics of decisions in criminal matters. It is argued that the efficiency of the entire system will largely depend on the establishment of information flows and the prioritization of tasks. Quality work requires the improvement of applied digital technologies capable of providing the necessary algorithms of the evidentiary process. The use of special software, including the use of artificial intelligence systems, is becoming increasingly relevant. The logistics of making decisions in criminal cases ideally represents an electronic assistant, endowed with artificial intelligence or in the form of a special computer program, capable, based on the determination of the forensic significance of the obtained digital information (electronic evidence), to offer the investigator solutions that can change the course of the investigation and transfer the entire information system in a new state. VL - 4 IS - 2 ER -