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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Nguyen, K. Tran, D. Wanli Ma Sharma, D. |
| Copyright Year | 2014 |
| Description | Author affiliation: Fac. of Educ., Sci., Technol. & Math., Univ. of Canberra, Canberra, ACT, Australia (Nguyen, K.; Tran, D.; Wanli Ma; Sharma, D.) |
| Abstract | 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. |
| Starting Page | 748 |
| Ending Page | 752 |
| File Size | 273331 |
| Page Count | 5 |
| File Format | |
| ISSN | 21579563 |
| e-ISBN | 9781479951512 |
| DOI | 10.1109/ICNC.2014.6975930 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-19 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Accuracy file fragment classification optimal data chunk size Digital forensics Support vector machine classification Entropy Vectors Security |
| Content Type | Text |
| Resource Type | Article |
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