Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Mary, Teena; Sreeja, C.S. Title A name given to the resource File fragment classification: A comprehensive survey of research advances Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Quantum Computing Models for Cybersecurity and Wireless Communications;pp.201-218 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1002/9781394271429.ch12" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/9781394271429.ch12</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85218841442?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85218841442?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Mary T., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Sreeja C.S., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India Description An account of the resource A crucial task in digital forensics is file fragment classification, which involves classifying file fragments into their respective types based on their content. It is integral to digital forensics and data recovery, where investigators reconstruct and analyze fragmented files to gather evidence in criminal cases, data breaches, or other cybercrimes. This comprehensive survey paper offers insights into the different methodologies used for file fragment classification, including but not restricted to specialized approaches, hierarchical classification, and neural networks. The paper also highlights the challenges in file fragment classification, such as the need for format standardization, limited training data, scalability, and noise and ambiguity. A research gap analysis of the existing literature was conducted, and it was identified that further research could be done to explore the effectiveness of different approaches for file fragment classification, including transfer learning, ensemble methods, and so on. 2025 Scrivener Publishing LLC. All rights reserved. Subject The topic of the resource Content-based analysis; Digital forensics; File fragment classification; Hierarchical classification; Neural network Publisher An entity responsible for making the resource available wiley Relation A related resource ISBN: 978-139427142-9; 978-139427139-9; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online