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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.
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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