<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michal Turčaník</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Network User Behaviour Analysis by Machine Learning Methods</style></title><secondary-title><style face="normal" font="default" size="100%">Information &amp; Security: An International Journal</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Clustering algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">Cybersecurity</style></keyword><keyword><style  face="normal" font="default" size="100%">machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">web page categorisation</style></keyword><keyword><style  face="normal" font="default" size="100%">web users analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><volume><style face="normal" font="default" size="100%">50</style></volume><pages><style face="normal" font="default" size="100%">66-78 </style></pages><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;margin-left:19.85pt;&quot;&gt;Cyber security is one of the prominent global challenges due to the significant increase in the number of cyberattacks over the last few decades. The amount of transferred data is growing, and a quick reaction to cyber incidents is needed. The paper is a contribution to this effort. There is a possibility to save time and resources by concentrating only on a subgroup of potential threats caused by a specific group of users. The main source of information about a selected group of users is the web access log file, where all the necessary data is stored. The contribution also presents the concept of preprocessing data from the log files to a form useful for clustering. In the next step, a density-based spatial clustering algorithm is applied to create the clusters. Clustering algorithms have been applied to many fields (marketing, business, etc.), but not for the purposes of cyber defence. The created clusters were analysed according to our definition of risky behaviour. After analysis of the clustering results, it was possible to select a potentially dangerous group of users in the specific cluster. The presented method has potential use in different areas of cyber defence and other applications where intelligent classification is required.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michal Turčaník</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Cyber Range for Armed Forces Education</style></title><secondary-title><style face="normal" font="default" size="100%">Information &amp; Security: An International Journal</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">cyber range</style></keyword><keyword><style  face="normal" font="default" size="100%">E&amp;T</style></keyword><keyword><style  face="normal" font="default" size="100%">education and training</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">46</style></volume><pages><style face="normal" font="default" size="100%">304-310</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Cyber security is one of the prominent global challenges due to significant increase in the number of cyberattacks over the last few decades. Cyber-security awareness and cyber security training are promoted by hyper-realistic virtual environments termed as cyber ranges. This article high-lights the concept of a cyber range. Cyber range for educational purposes in the armed forces has been proposed taking into account the important parameters a cyber range should incorporate. The author takes into ac-count the use cases, the topology and software tools of the newly created cyber range.</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><section><style face="normal" font="default" size="100%">304</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michal Turčaník</style></author><author><style face="normal" font="default" size="100%">Martin Javurek</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cryptographic Key Generation by Genetic Algorithms</style></title><secondary-title><style face="normal" font="default" size="100%">Information &amp; Security: An International Journal</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">cryptographic keys generation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">tree parity machine</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><volume><style face="normal" font="default" size="100%">43</style></volume><pages><style face="normal" font="default" size="100%">54-61</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;One of the security conditions of Vernam&amp;rsquo;s cipher is that the encryption key must be greater than or equal to the open text we want to encrypt. At the same time, this key must not be repeated in another encryption. Then, each change of the encryption key adds security to the encryption process. If a cipher is changed several times while encrypting a single open text, it becomes very difficult to decrypt the message. Therefore, our goal is to design a mechanism to generate an encryption key using a Tree Parity Machine and a Genetic Algorithm that will be able to create the same encryption keys on both sides that enter the encryption process. These keys should change during encryption. One of the first tasks is to create an input population for the genetic algorithm from the synchronized Tree parity machine. Therefore, this article presents one of the possible ways to create an input population without using too many synchronizing TPMs.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><section><style face="normal" font="default" size="100%">54</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Javurek Martin</style></author><author><style face="normal" font="default" size="100%">Michal Turčaník</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Synchronization Verification Improvement of Two Tree Parity Machines Using Polynomial Function</style></title><secondary-title><style face="normal" font="default" size="100%">2018 New Trends in Signal Processing (NTSP)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><pub-location><style face="normal" font="default" size="100%">Demanovska Dolina (2018)</style></pub-location><pages><style face="normal" font="default" size="100%">1-5</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michal Turčaník</style></author><author><style face="normal" font="default" size="100%">Javurek Martin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Hash Function Generation by Neural Network</style></title><secondary-title><style face="normal" font="default" size="100%">2016 New Trends in Signal Processing (NTSP)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><pub-location><style face="normal" font="default" size="100%">Demanovska Dolina</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Javurek Martin</style></author><author><style face="normal" font="default" size="100%">Michal Turčaník</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Synchronization of Two Tree Parity Machines</style></title><secondary-title><style face="normal" font="default" size="100%">2016 New Trends in Signal Processing (NTSP)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><pub-location><style face="normal" font="default" size="100%">Demanovska Dolina (2016)</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michal Turčaník</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Optimalization of the Artificial Neural Network and Production Systems by Genetic Algorithms</style></title><secondary-title><style face="normal" font="default" size="100%">MATLAB 2002: Proceedings of the Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><pub-location><style face="normal" font="default" size="100%">Prague</style></pub-location><pages><style face="normal" font="default" size="100%">562-568</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>