Detection of Encrypted Data Based on Support Vector Data Description

Author

Juan Meng ; Yuhuan Zhou ; Zhisong Pan

Author_Institution

Coll. of Command Inf. Syst., PLA Univ. of Sci. & Technol., Nanjing, China

fYear

2013

fDate

13-15 Dec. 2013

Firstpage

187

Lastpage

191

Abstract

Data encryption has been widely used. It is important to detect encrypted data. We present a method for detection of encrypted data based on the Support Vector Data Description (SVDD) algorithm. The SVDD is a single class, non-parametric approach for modeling the support of a distribution. We apply the SVDD techniques for detection of encrypted data. Experimental results show that the SVDD can be adopted as an effective tool for detection of encrypted data.

Keywords

cryptography; nonparametric statistics; support vector machines; SVDD algorithm; SVDD technique; data encryption; encrypted data detection; nonparametric approach; support vector data description; Cryptography; Data models; Feature extraction; Kernel; NIST; Support vector machines; Training; NIST SP800-22 standard; detection of encrypted data; support vector data description;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Cloud and Big Data (CBD), 2013 International Conference on

Conference_Location

Nanjing

Print_ISBN

978-1-4799-3260-3

Type

conf

DOI

10.1109/CBD.2013.17

Filename

6824594

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=685918