Results 211 to 220 of about 143,436 (230)

File Fragment Classification Using Grayscale Image Conversion and Deep Learning in Digital Forensics [PDF]

2018 IEEE Security and Privacy Workshops (SPW), 2018
File fragment classification is an important step in digital forensics. The most popular method is based on traditional machine learning by extracting features like N-gram, Shannon entropy or Hamming weights. However, these features are far from enough to classify file fragments.
Lucas C K Hui, Dong Liu, En Zhang
exaly   +2 more sources

Some of the next articles are maybe not open access.

Related searches:

Intra- and inter-sector contextual information fusion with joint self-attention for file fragment classification

Knowledge-Based Systems
File fragment classification (FFC) aims to identify the file type of file fragments in memory sectors, which is of great importance in memory forensics and information security. Existing works focused on processing the bytes within sectors separately and ignoring contextual information between adjacent sectors.
Yi Wang, Xiao Liu, Kim-Hui Yap
exaly   +3 more sources

Hybrid Feature Selection Method for Improving File Fragment Classification

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
openaire   +2 more sources

Optimizing File Fragment Classification By Mitigating Class Imbalance Problem

2024 1st International Conference on Innovative Engineering Sciences and Technological Research (ICIESTR)
Shahid Alam
exaly   +2 more sources

Targeted Feature Engineering for Image File Fragment Classification

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
openaire   +1 more source

A Novel Machine Learning Approach For File Fragments Classification

2022
Identifying types of manipulated or corrupted file fragments in isolation from their context is an essential task in digital forensics. In traditional file type identification, metadata, such as file extensions and header and footer signatures, is used.
openaire  

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
Soha B. Sandouka, Muhamad Felemban
openaire   +1 more source

Light-Weight File Fragments Classification Using Depthwise Separable Convolutions

2022
Kunwar Muhammed Saaim, Muhamad Felemban, Saleh Alsaleh, Ahmad Almulhem   +3 more
openaire   +1 more source

A Language-Independent Approach to Classification of Textual File Fragments: Case Study of Persian, English, and Chinese Languages

2021 11th International Conference on Computer Engineering and Knowledge (ICCKE), 2021
Fatemeh Mansouri Hanis, Hamidreza Khoshvaghti, Mehdi Teimouri, Hadi Veisi   +3 more
openaire   +1 more source

Data Fragment Classification of High Entropy Files Using Machine Learning

2023
M. Sunitha, K. Srinivas, T. Adilakshmi, D. Baswaraj, Ashish Perala, Visista Bellamkonda   +5 more
openaire   +1 more source