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Analytics in Digital Forensics and eDiscovery Software - DevOps, Opportunities and Challenges
Sundar Krishnan, Narasimha Shashidhar, Cihan Varol, ABM Rezbaul Islam
Pages - 16 - 27     |    Revised - 30-06-2022     |    Published - 01-08-2022
Volume - 13   Issue - 1    |    Publication Date - August 2022  Table of Contents
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KEYWORDS
Digital Forensic Analytics, Digital Forensics, Sexual Harassment, Supervised Learning, Hybrid Learning, Unsupervised Learning, Legal Analytics, ediscovery, Electronic Stored Information, Case Investigation, Sentiment Analysis, Financial Fraud, Securities Fraud.
ABSTRACT
Digital forensic and eDiscovery software have embraced analytics such as machine learning and neural networks to speed up the investigation and thereby reduce costs. Since the integrity of forensic evidence is paramount to the investigation, care should be taken when working with evidence in an analytical experiment setting. Data mined from case evidence can provide different clues and together with automation, legal teams can better prepare legal arguments for the courtroom. In this paper, the authors develop a custom digital forensic software that leverages analytics and outline few development challenges and opportunities encountered along the way.
Baggili, I., & Behzadan, V. (2019). Founding The Domain of AI Forensics. CEUR Workshop Proceedings, 2560, 31–35. https://doi.org/10.48550/arxiv.1912.06497.
BestPracticesFor Seizing Electronic Evidence, A Pocket Guide for First Responders. (2018). Retrieved from https://www.cwagweb.org/wp-content/uploads/2018/05/BestPracticesforSeizingElectronicEvidence.pdf.
Bhatt, P. (2017). Machine Learning Forensics: A New Branch Of Digital Forensics. International Journal of Advanced Research in Computer Science, 8(8), 217–222. https://doi.org/10.26483/IJARCS.V8I8.4613.
Carty, D. (2021). Training, Validation and Testing Data Explained. Retrieved May 4, 2022, from https://www.applause.com/blog/training-data-validation-data-vs-test-data.
Corporation, O. (n.d.). What is Big Data?
Hall, S. W., Sakzad, | Amin, Kim-Kwang, |, & Choo, R. (2022). Explainable artificial intelligence for digital forensics. Wiley Interdisciplinary Reviews: Forensic Science, 4(2), e1434. https://doi.org/10.1002/WFS2.1434.
Herman, M., Ahsen, M. I., Salim, M., Jackson, R. H., Hurst, M. R., Leo, R., … Sardinas, R. (n.d.). NIST Cloud Computing Forensic Science Challenges. https://doi.org/10.6028/NIST.IR.8006.
Interpol. (n.d.). Catalogue of Digital Forensic Tools. Retrieved from https://www.interpol.int/content/download/16480/file/Catalogue of Digital Forensic Tools.pdf.
Jarrett, A., & Choo, K.-K. R. (2021). The impact of automation and artificial intelligence on digital forensics. Wiley Interdisciplinary Reviews: Forensic Science, 3(6), e1418. https://doi.org/10.1002/WFS2.1418.
Krishnan, S. (n.d.). Project • GitHub. Retrieved May 6, 2022, from https://github.com/kshsus.
Krishnan, S., Shashidhar, N., Varol, C., & Islam, A. R. (2022a). A Novel Text Mining Approach to Securities and Financial Fraud Detection of Case Suspects. International Journal of Artificial Intelligence and Expert Systems, 10(3). Retrieved from https://www.cscjournals.org/journals/IJAE/issues-archive.php.
Krishnan, S., Shashidhar, N., Varol, C., & Islam, A. R. (2022b). A Novel Text Mining Approach to Sexual Harassment Detection of Case Suspects. International Journal of Artificial Intelligence and Expert Systems, 10(3). Retrieved from https://www.cscjournals.org/journals/IJAE/issues-archive.php.
Krishnan, S., Shashidhar, N., Varol, C., & Islam, A. R. (2022c). Sentiment Analysis of Case Suspects in Digital Forensics and Legal Analytics. International Journal of Security, 13(1). Retrieved from https://www.cscjournals.org/journals/IJS/issues-archive.php.
Krishnan, S., Shashidhar, N., Varol, C., & Rezbaul Islam, A. (2021). Evidence Data Preprocessing for Forensic and Legal Analytics. International Journal of Computational Linguistics (IJCL), 12(2), 24–34. Retrieved from https://www.cscjournals.org/library/manuscriptinfo.php?mc=IJCL-122.
Mitchell, F. (2010). The use of Artificial Intelligence in digital forensics: An introduction - SAS-Space. Digital Evidence and Electronic Signature Law Review, 7. Retrieved from https://sas-space.sas.ac.uk/5533/.
Pan, L., & Batten, L. (2005). DIGITAL FORENSIC RESEARCH CONFERENCE Reproducibility of Digital Evidence in Forensic Investigations.
Randomization | Data Preparation and Feature Engineering for Machine Learning | Google Developers. (n.d.). Retrieved May 2, 2022, from https://developers.google.com/machine-learning/data-prep/construct/sampling-splitting/randomization.
Ron J, R. J. (n.d.). The Use Of Artificial Intelligence In Digital Forensics. Retrieved April 27, 2022, from https://www.exterro.com/blog/the-use-of-artificial-intelligence-in-digital-forensics.
Rughani, P. H. (2017). ARTIFICIAL INTELLIGENCE BASED DIGITAL FORENSICS FRAMEWORK. International Journal of Advanced Research in Computer Science, 8(8).
Study Github Repository. (n.d.).
Tour of Data Sampling Methods for Imbalanced Classification. (n.d.). Retrieved May 2, 2022, from https://machinelearningmastery.com/data-sampling-methods-for-imbalanced-classification/.
Mr. Sundar Krishnan
Department of Computer Science, Sam Houston State University, Huntsville, TX - United States of America
skrishnan@shsu.edu
Associate Professor Narasimha Shashidhar
Department of Computer Science, Sam Houston State University, Huntsville, TX - United States of America
Professor Cihan Varol
Department of Computer Science, Sam Houston State University, Huntsville, TX - United States of America
Associate Professor ABM Rezbaul Islam
Department of Computer Science, Sam Houston State University, Huntsville, TX - United States of America


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