<?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%">Plamena Andreeva</style></author><author><style face="normal" font="default" size="100%">George Georgiev</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fuzzy Control Based on Cluster Analysis and Dynamic Programming</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%">Decision-making</style></keyword><keyword><style  face="normal" font="default" size="100%">Dynamic programming.</style></keyword><keyword><style  face="normal" font="default" size="100%">fuzzy control</style></keyword><keyword><style  face="normal" font="default" size="100%">Knowledge Based Systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Learning in Fuzzy Environment</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1999</style></year><pub-dates><date><style  face="normal" font="default" size="100%">1999</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">91-107</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper focuses on fuzzy control of a class of nonlinear systems, which are characterized by model uncertainty and inequality model constraints. The associated Intelligent Information System (IIS) is designed to store the results from possible training made by an expert and distributed via network. The paper considers cluster analysis for such a system, based on Bezdek’s fuzzy cluster method (FCM). The proposed method is used to classify the input data and to extract the rules. 
An example of fuzzy control for autonomous mobile system in 3D space is explored and the results from the decision using the method of dynamic programming in fuzzy environment are shown. The synthesized algorithm guides an autonomous vehicle in 3D space which pursues an object and evades an obstacle. The fuzzy control is based on determination of a maximizing decision by using dynamic programming. The maximizing decision is defined as a point in the space of alternatives at which the membership function of a fuzzy decision attains its maximum value. The purpose of the presented algorithm is to demonstrate a fuzzy method for determination of the trajectory of the dynamic object.</style></abstract></record></records></xml>