Detection of Encrypted Data Based on Support Vector Data Description
Juan Meng, Yuhuan Zhou, Zhisong Pan · 2013
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.