Results 21 to 30 of about 55,996 (210)

Approaches to the classification of high entropy file fragments [PDF]

Digital Investigation, 2013
In this paper we propose novel approaches to the problem of classifying high entropy file fragments. We achieve 97% correct classification for encrypted fragments and 78% for compressed. Although classification of file fragments is central to the science of Digital Forensics, high entropy types have been regarded as a problem. Roussev and Garfinkel [1]
Philip Penrose, Richard Macfarlane, William J. Buchanan   +2 more
openalex   +3 more sources

ByteRCNN: Enhancing File Fragment Type Identification With Recurrent and Convolutional Neural Networks

IEEE Access, 2023
File fragment type identification is an important step in file carving and data recovery. Machine learning techniques, especially neural networks, have been utilized for this problem, some with very promising results.
Kristian Skracic, Juraj Petrovic, Predrag Pale   +2 more
doaj   +3 more sources

Targeted feature engineering for image file fragment classification

Array
Accurate classification of file fragments is a critical challenge in digital forensics, particularly when formats exhibit similar structural or statistical properties. Recent deep learning approaches achieve high within-dataset accuracy but suffer from poor generalization, with performance degrading significantly on external datasets from different ...
Alireza Chalechale, Mehdi Teimouri
  +4 more sources

Hierarchical Deep Learning for File Fragment Classification [PDF]

Electronics
File fragment classification is crucial in digital forensics, aiding in the recovery and reconstruction of fragmented files, which serve as key evidence; while deep learning techniques have advanced in this area, challenges remain, particularly regarding the consideration of inter-file-type relationships and the granularity of classification.
Bailin Zou, HuiYi XU Yan LIU
openalex   +2 more sources

Transformer-Based File Fragment Type Classification for File Carving in Digital Forensics

European Conference on Cyber Warfare and Security
The recovery and reconstruction of fragmented data is a critical challenge in digital forensics, particularly when dealing with incomplete, corrupted, or partially deleted files in large-scale cybercrime investigations. Accurate classification of file fragment types is essential for reconstructing critical evidence, especially in environments ...
Andrey Guzhov, Christoph Tobias Wirth
openalex   +3 more sources

Size‐agnostic file fragment classification via fixed byte slicing

ETRI Journal
Abstract File fragment classification is an essential task of identifying the file type given an incomplete binary file fragment. Despite their importance, existing deep‐learning methods face two key limitations: (1) extensive hyperparameter tuning for varying fragment sizes and (2) considerably degraded inference
HyeongSik Kim, Sisung Liu, Je Hyeong Hong   +2 more
openalex   +2 more sources

A File Fragment Classification Method Based on Grayscale Image

Journal of Computers, 2014
File fragment classification is an important and difficult problem in digital forensics. Previous works in this area mainly relied on specific byte sequences in file headers and footers, or statistical analysis and machine learning algorithms on data from the middle of the file.
Tantan Xu, Ming Xu, Yizhi Ren, Jian Xu, Haiping Zhang, Ning Zheng   +5 more
openalex   +2 more sources

Support Vector Machines-based classification of video file fragments [PDF]

Journal of the Korea Academia-Industrial cooperation Society, 2015
Hyun-Suk Kang, Youngseok Lee
openalex   +3 more sources

Leveraging Federated Learning for File Fragments Classification Based on Depthwise Separable Convolutions [PDF]

Proceedings of the 7th International Conference on Future Networks and Distributed Systems, 2023
Soha B. Sandouka, Muhamad Felemban
openalex   +2 more sources

Impact of Visual Content Diversity on Cross-Dataset Generalization in Image File Fragment Classification

File fragment classification (FFC) identifies the type of binary fragments without metadata or headers, representing a core challenge in digital forensics. While recent FFC methods often achieve high accuracy within their training domains, they consistently struggle to generalize to unseen datasets.
Behnam Tavassoli, Mehdi Teimouri
openalex   +2 more sources