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 Article Faculty Publications -Articles 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 Attention and Representation Learning in Byte-Level Digital Forensics: A Survey of Methods, Challenges, and Applications Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived International Journal of Advanced Computer Science and Applications;Volume;17;Issue;2;pp.113-124 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.14569/IJACSA.2026.0170213" target="_blank" rel="noreferrer noopener">https://doi.org/10.14569/IJACSA.2026.0170213</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105039182557?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105039182557?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 University, Karnataka, Bengaluru, 560029, India; Sreeja C.S., Center for Quantum Technologies and Complex Systems (CQTCS), Christ University, Karnataka, Bengaluru, 560029, India Description An account of the resource Byte-level analysis has become an essential capability in digital forensics, enabling content-based investigation when file system metadata, headers, or structural information are unavailable or unreliable. Recent advances in deep learning allow forensic systems to learn discriminative features directly from raw byte streams; however, the growing diversity of representation strategies, architectural designs, and attention mechanisms makes it difficult to assess their relative effectiveness and practical suitability. This study presents a structured survey of representation learning and attention-based approaches for byte-level digital forensic analysis. We examine statistical, embedding-based, image-based, sequential, and hybrid representations, and analyze how architectural choices and attention mechanisms influence performance, robustness, and scalability. Across the literature, hybrid representations combined with lightweight convolutional backbones and selective attention mechanisms consistently provide a favorable balance between accuracy and computational efficiency. The survey also reviews key forensic applications, including file fragment classification, malware and binary analysis, network payload forensics, and encrypted or compressed data triage. In addition, we critically discuss challenges related to distribution shift, dataset bias, adversarial vulnerability, interpretability, and reproducibility, along with practical considerations for deployment in large-scale forensic pipelines. By synthesizing architectural trends, operational constraints, and reliability concerns, this work identifies critical research gaps and provides a structured foundation for the development of robust and trustworthy byte-level forensic learning systems. (2026), (Science and Information Organization). All rights reserved. Subject The topic of the resource attention mechanisms; Byte-level digital forensics; deep learning; file fragment classification; forensic robustness; representation learning Publisher An entity responsible for making the resource available Science and Information Organization Relation A related resource ISSN: 2158107X; Language A language of the resource English Type The nature or genre of the resource Article 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