Results 181 to 190 of about 103,778 (222)

File Fragment Classification with Focus on OLE and OOXML classes

2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO), 2020
Classification of file fragments is a crucial step in digital forensics and determining file types based on available data fragments. Currently explored file fragment classification methods other than forensic hand-examination rely on machine learning techniques. Those methods most commonly use features based on byte frequency distribution as inputs in
Skračić, Kristian, Rukavina, Filip, Miličić, Karlo, Petrović, Juraj, Pale, Predrag   +4 more
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File Fragment Type Classification by Bag-Of-Visual-Words

ISC Int. J. Inf. Secur., 2021
File fragment’s type classification in the absence of header and file system information, is a major building block in various solutions devoted to file carving, memory analysis and network forensics. Over the past decades, a substantial amount of effort has been put into developing methods to classify file fragments.
Erfan, Mina, Jalili, Saeed
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Optimizing File Fragment Classification By Mitigating Class Imbalance Problem

2024 1st International Conference on Innovative Engineering Sciences and Technological Research (ICIESTR)
In cyber forensics, one of the main challenges of file fragment classification is the proliferation of file formats. This leads to a multinomial classification problem that aggravates the class imbalance problem in classifying file fragments.
Shahid Alam, Zeynep Altiparmak
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Exploring 1D Data Augmentation Techniques for Improved File Fragment Classification

2025 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)
File Fragment Classification (FFC) is essential for digital forensics, facilitating file type identification without relying on metadata or intact headers.
Mincheol Kim, Sisung Liu, Hyeongsik Kim, Je Hyeong Hong   +3 more
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XMP: A Cross-Attention Multi-Scale Performer for File Fragment Classification

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
File fragment classification (FFC) is the task of identifying the file type given a small fraction of binary data, and serves a crucial role in digital forensics and cybersecurity. Recent studies have adopted convolutional neural networks (CNNs) for this
Jeong Gyu Park, Sisung Liu, Je Hyeong Hong   +2 more
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A Cross-Attention Multi-Scale Performer With Gaussian Bit-Flips for File Fragment Classification

IEEE Transactions on Information Forensics and Security
File fragment classification is a crucial task in digital forensics and cybersecurity, and has recently achieved significant improvement through the deployment of convolutional neural networks (CNNs) compared to traditional handcrafted feature-based ...
Sisung Liu, Jeong Gyu Park, Hyeongsik Kim, Je Hyeong Hong   +3 more
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Leveraging Federated Learning for File Fragments Classification Based on Depthwise Separable Convolutions

Proceedings of the 7th International Conference on Future Networks and Distributed Systems, 2023
In digital forensics, file fragment classification plays a crucial role in the file carving process. Recently, convolutional neural network based models have been utilized for file fragment classification to improve the classification accuracy.
Soha B Sandouka, Muhamad Felemban
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Hybrid Feature Selection Method for Improving File Fragment Classification

SGAI Conferences, 2019
Identifying types of file fragments in isolation from their context is an essential task in digital forensic analysis and can be done with several methods. One common approach is to extract various types of features from file fragments as inputs for classification algorithms. However, this approach suffers from dimensionality curse as the number of the
Algurashi, Alia, Wang, Wenjia
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ByteGT: A Hybrid Sequential-Attention Network for Enhancing File Fragment Classification on Raw Data

2025 International Joint Conference on Neural Networks (IJCNN)
In digital forensics practice, the precise determination of file fragment types serves as an essential prerequisite for successful file carving. Recent advancements in neural network-based methods have shown promise in this area, though challenges remain
Yuhao Zhang, Fei Wang, Shuhui Chen, Ziling Wei   +3 more
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A Multi-Task Hybrid Deep Learning Based Framework for Robust File Fragment Classification in Digital Forensics

2025 6th International Conference for Emerging Technology (INCET)
File fragment categorization is integral to the current digital forensics, as the failure to precisely retrieve and categorize fragmented data can compromise criminal investigations, cybersecurity incident responses, especially the reconstruction of ...
Indrani Sen Toma, Amit Hasan Tonmoy, Angkita Bhowmik, Sowmik Sarker   +3 more
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