of unused packet fields and information encoding in traffic behaviour. The first element

of unused packet fields and information encoding in traffic behaviour. The first element

Quantum key distribution (QKD) is based on the laws of quantum physics and therefore it can guarantee the highest level of security. It is used to establish the key that is used for further symmetrical encryption. Since QKD consists of several phases in which the key is reduced, it is necessary to define the equation by which the length of the raw key is calculated. In this paper, we analyse all QKD phases with an emphasis on the explanation of the process of shortening the initial key. The results are verified with a large number of tests using a quantum cryptography simulator. DOI: http://dx.doi.org/10.5755/j01.eee.21.6.13768

Global System for Mobile communication (GSM) is the most widespread technology for mobile communications in the world and serving over 7 billion users. Since first publication of system documentation there has been notified a potential safety problem’s occurrence. Selected types of attacks, based on the analysis of the technical feasibility and the degree of risk of these weaknesses, were implemented and demonstrated in laboratory of the VSB-Technical University of Ostrava, Czech Republic. These vulnerabilities were analyzed and afterwards possible attacks were described. These attacks were implemented using open-source tools, software programmable radio USRP (Universal Software RadioPeripheral) and DVB-T (Digital Video Broadcasting – Terrestrial) receiver. GSM security architecture is being scrutinized since first public releases of its specification mainly pointing out weaknesses in authentication and ciphering mechanisms. This contribution also summarizes practically proofed and used scenarios that are performed using opensource software tools and variety of scripts mostly written in Python. Main goal of this paper is in analyzing security issues in GSM network and practical demonstration of selected attacks.

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.

Emotional states of humans and their impact on physiological and neurological characteristics are discussed in this paper. This problem is the goal of many teams who have dealt with this topic. Nowadays, it is necessary to increase the accuracy of methods for obtaining information about correlations between emotional state and physiological changes. To be able to record these changes, we focused on two majority emotional states. Studied subjects were psychologically stimulated to neutral - calm and then to the stress state. Electrocardiography, Electroencephalography and blood pressure represented neurological and physiological samples that were collected during patient’s stimulated conditions. Speech activity was recording during the patient was reading selected text. Feature extraction was calculated by speech processing operations. Classifier based on Gaussian Mixture Model was trained and tested using Mel-Frequency Cepstral Coefficients extracted from the patient's speech. All measurements were performed in a chamber with electromagnetic compatibility. The article discusses a method for determining the influence of stress emotional state on the human and his physiological and neurological changes.

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.