File fragment classification: A comprehensive survey of research advances

Title

File fragment classification: A comprehensive survey of research advances

Creator

Mary, Teena; Sreeja, C.S.

Description

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.

Source

Quantum Computing Models for Cybersecurity and Wireless Communications;pp.201-218

Date

01-01-2025

Publisher

wiley

Subject

Content-based analysis; Digital forensics; File fragment classification; Hierarchical classification; Neural network

Coverage

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

Rights

All Open Access; Gold Open Access

Relation

ISBN: 978-139427142-9; 978-139427139-9;

Format

online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1002/9781394271429.ch12

https://www.scopus.com/pages/publications/85218841442?origin=resultslist