Title :
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
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;
Conference_Titel :
Advanced Cloud and Big Data (CBD), 2013 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4799-3260-3
DOI :
10.1109/CBD.2013.17
