DocumentCode :
2842505
Title :
A new approach to content-based file type detection
Author :
Amirani, M.C. ; Toorani, Mohsen ; Beheshti, A.
Author_Institution :
Dept. of Electr. Eng., Iran Univ. of Sci. & Technol. (IUST), Tehran, Iran
fYear :
2008
fDate :
6-9 July 2008
Firstpage :
1103
Lastpage :
1108
Abstract :
File type identification and file type clustering may be difficult tasks that have an increasingly importance in the field of computer and network security. Classical methods of file type detection including considering file extensions and magic bytes can be easily spoofed. Content-based file type detection is a newer way that is taken into account recently. In this paper, a new content-based method for the purpose of file type detection and file type clustering is proposed that is based on the PCA and neural networks. The proposed method has a good accuracy and is fast enough.
Keywords :
feature extraction; file organisation; neural nets; pattern clustering; principal component analysis; security of data; PCA; computer security; content-based file type detection; feature extraction; file extension; file type clustering; file type identification; magic bytes; network security; neural network; Accuracy; Artificial neural networks; Computers; Feature extraction; Neurons; Principal component analysis; Probability density function;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computers and Communications, 2008. ISCC 2008. IEEE Symposium on
Conference_Location :
Marrakech
ISSN :
1530-1346
Print_ISBN :
978-1-4244-2702-4
Type :
conf
DOI :
10.1109/ISCC.2008.4625611
Filename :
4625611
Link To Document :
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