General Categorization of data mining techniques on Steganalysis

The aim of this section is to present an immediate wide spread picture of the importance of mixing data mining techniques. By using data mining algorithms we detect secret and hidden concealed message through steganalysis. We have several kinds of data types assigned as Domain that should be considered such as: Image, Audio, Text, Video and Protocol. This classification is based on data mining techniques extended on steganalysis in this domain which are used to detect the presence of embedded messages in stego images by steganography techniques. Under each of them, the domains are further sub-divided into data mining techniques. The whole hierarchy of the categorization is shown in fig 1:

Fig. 1. The hierarchy of the General Categorization of Data Mining techniques on steganalysis.

These sub-domains (data mining techniques) are sub-divided into approaches which are applied in:

• Classification, which has been divided into neural network (NN), k-nearest neighbour (KNN), Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB) approaches. This category has itself a hierarchy which is shown in Fig. 2.
• Clustering, which has been divided into K-means, agglomeration and random algorithms.
• Other data mining tasks, such as regression approaches and etc.

Fig. 2. The hierarchy of the category classification approach.

 

Source : ISBN 978-953-51-0840-5