TY - GEN
T1 - The impact of data fragment sizes on file type recognition
AU - TRAN, Dat
AU - MA, Wanli
AU - SHARMA, Dharmendra
PY - 2014
Y1 - 2014
N2 - Determining the original file type of data fragments helps data recovery, spam detection, virus scanning, and network monitoring operations. In many cases, only unordered fragments of the original file are available for investigation. Therefore, we can only base on the content of a fragment to identify its file type. However, data fragments come with different sizes, as they may be the residual data recovered from storage media or network packets. It is stated that identifying the file type of larger fragments is easier than the smaller size ones [1]. Therefore, it is important to study the impact of data fragment sizes on file type recognition. In this paper, we study the results of applying machine learning technique to identify file types of data fragments of different sizes in order to find the minimum size required for file type recognition purpose.
AB - Determining the original file type of data fragments helps data recovery, spam detection, virus scanning, and network monitoring operations. In many cases, only unordered fragments of the original file are available for investigation. Therefore, we can only base on the content of a fragment to identify its file type. However, data fragments come with different sizes, as they may be the residual data recovered from storage media or network packets. It is stated that identifying the file type of larger fragments is easier than the smaller size ones [1]. Therefore, it is important to study the impact of data fragment sizes on file type recognition. In this paper, we study the results of applying machine learning technique to identify file types of data fragments of different sizes in order to find the minimum size required for file type recognition purpose.
KW - Digital forensics
KW - File fragment classification
KW - Optimal data chunk size
UR - http://ieeexplore.ieee.org/document/6975930/
UR - http://www.scopus.com/inward/record.url?scp=84926659803&partnerID=8YFLogxK
U2 - 10.1109/ICNC.2014.6975930
DO - 10.1109/ICNC.2014.6975930
M3 - Conference contribution
SN - 9781479951505
T3 - 2014 10th International Conference on Natural Computation, ICNC 2014
SP - 748
EP - 752
BT - 2014 10th International Conference on Natural Computation, ICNC 2014
A2 - Han, Shuhua
A2 - Li, Tao
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - China
T2 - 10th International Conference on Natural Computation 2014
Y2 - 19 August 2014 through 21 August 2014
ER -