0% Complete
Home
/
11th International Conference on Computer and Knowledge Engineering
A Language-Independent Approach to Classification of Textual File Fragments: Case Study of Persian, English, and Chinese Languages
Authors :
Fatemeh Mansouri Hanis
1
Hamidreza Khoshvaghti
2
Mehdi Teimouri
3
Hadi Veisi
4
1- University of Tehran
2- University of Tehran
3- University of Tehran
4- University of Tehran
Keywords :
Classification, File fragments, language-dependent file type identification, textual files, context language, file format.
Abstract :
With the advent of communications systems in recent decades, the transmission of electronic files on computer networks has dramatically increased. In this situation, identifying the type of files is important in many applications such as digital forensics and file carving. The state-of-the-art methods for identifying the file type of a file fragment are based on the content of the fragments. To the best of the authors' knowledge, there is no study addressing the effect of context language in identifying the file type of textual file fragments. In this paper, we have considered a machine learning approch for the classification among five types of common text file formats: PDF, DOC, DOCX, RTF, and TXT. Also, we have examined the effect of context language on the classification of the file fragments. Two scenarios are considered. In the first one, the language for both training and testing phases are the same, that the best results are achieved; the accuracies of the test for Persian, English, and Chinese languages are 85.6%, 76.4%, 86.1%, respectively. In the second scenario, the languages of training and testing sets are not the same, in which the training is done using one language and the evaluation is performed on the two other languages. In this case, the average accuracy values for Persian, English, and Chinese languages are 60.0%, 58.5%, and 71.4%, respectively. The evaluations of the second scenario show that the language-independent machine learning approach is robust in the identification of DOC, DOCX, and RTF formats.
Papers List
List of archived papers
Efficient Prediction of Cardiovascular Disease via Extra Tree Feature Selection
Mina Abroodi - Mohammad Reza Keyvanpour - Ghazaleh Kakavand Teimoory
Deep Learning Feature Extraction for COVID-19 Detection Algorithm using Computerized Tomography Scan
Maisarah Mohd Sufian - Ervin Gubin Moung - Chong Joon Hou - Ali Farzamnia
Facial Emotion Recognition Under Mask Coverage Using a Data Augmentation Technique
Aref Farhadipour - Pouya Taghipour
FGM Copula based Analysis of Coverage Region for Wireless Three-User Multiple Access Channel with Correlated Channel Coefficients
Mona Sadat Mohsenzadeh - Ghosheh Abed Hodtani
Energy-Aware Dynamic Digital Twin Placement in Mobile Edge Computing
Mahdi Hematyar - Zeinab Movahedi
A Systematic Embedded Software Design Flow for Robotic Applications
Navid Mahdian - Seyed-Hosein Attarzadeh-Niaki - Armin Salimi-Badr
Adversarial Robustness Evaluation with Separation Index
Bahareh Kaviani Baghbaderani - Afsaneh Hasanebrahimi - Ahmad Kalhor - Reshad Hosseini
FAHP-OF: A New Method for Load Balancing in RPL-based Internet of Things (IoT)
Mohammad Koosha - Behnam Farzaneh - Emad Alizadeh - Shahin Farzaneh
Optimizing MR Image Registration for Accurate Brain Volume Measurement in Children with Autism Spectrum Disorder
Shiva Sanati - Mahdi Saadatmand
No-Reference Video Quality Assessment by Deep Feature Maps Relations
Amir Hossein Bakhtiari - Azadeh Mansouri
more
Samin Hamayesh - Version 41.3.1