This paper concerns available steganographic techniques that can be used for sending hidden data through public network. Typically, in steganographic communication it is advised to use popular/often used method for sending hidden data and amount of that data need to be high as much as possible. We confirmed this by choosing a Domain Name System (DNS) as a vital protocol of each network and choosing Distributed denial of service (DDoS) attacks that are most popular network attacks currently represented in the world. Apart from characterizing existing steganographic methods we provide new insights by presenting two new techniques. The first one is network steganography solution which exploits free/unused protocols fields and is known for IP, UDP or TCP protocols, but has never been applied to DNS (Domain Name Server) which are the fundamental part of network communications. The second explains the usage of DNS Amplification DDoS Attack to send seamlessly data through public network. The calculation that was performed to estimate the total amount of data that can be covertly transferred by using these technique, regardless of steganalysis, is included in this paper.

This paper deals with the comparison of different methods of speech features extraction for a neural network classifier. We have used a Kohohen self-organizing feature map (SOM) for output-stage classifier which is a specific type of artificial neural nets. The result of this research deals with the accuracy of emotion classifier and compares the two input combinations.