Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Content Based File Type Detection Algorithms
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 9 - Volume 9
Classifying file type of stream ciphers in depth using neural networks
AICCSA '05 Proceedings of the ACS/IEEE 2005 International Conference on Computer Systems and Applications
SÁDI - Statistical Analysis for Data Type Identification
SADFE '08 Proceedings of the 2008 Third International Workshop on Systematic Approaches to Digital Forensic Engineering
Statistics over features: EEG signals analysis
Computers in Biology and Medicine
Proceedings of the 2010 ACM Symposium on Applied Computing
EEG signal classification using PCA, ICA, LDA and support vector machines
Expert Systems with Applications: An International Journal
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Predicting the types of file fragments
Digital Investigation: The International Journal of Digital Forensics & Incident Response
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Digital information is packed into files when it is going to be stored on storage media. Each computer file is associated with a type. Type detection of computer data is a building block in different applications of computer forensics and security. Traditional methods were based on file extensions and metadata. The content-based method is a newer approach with the lowest probability of being spoofed and is the only way for type detection of data packets and file fragments. In this paper, a content-based method that deploys principle component analysis and neural networks for an automatic feature extraction is proposed. The extracted features are then applied to a classifier for the type detection. Our experiments show that the proposed method works very well for type detection of computer files when considering the whole content of a file. Its accuracy and speed is also significant for the case of file fragments, where data is captured from random starting points within files, but the accuracy differs according to the lengths of file fragments. Copyright © 2012 John Wiley & Sons, Ltd.