<?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%">Sergiy Lysenko</style></author><author><style face="normal" font="default" size="100%">Oleg Savenko</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Software for Computer Systems Trojans Detection as a Safety-Case Tool</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%">antivirus software.</style></keyword><keyword><style  face="normal" font="default" size="100%">artificial immune systems</style></keyword><keyword><style  face="normal" font="default" size="100%">behavioural model</style></keyword><keyword><style  face="normal" font="default" size="100%">Fuzzy Logic</style></keyword><keyword><style  face="normal" font="default" size="100%">Trojan life cycle</style></keyword><keyword><style  face="normal" font="default" size="100%">Trojans</style></keyword><keyword><style  face="normal" font="default" size="100%">Trojans detection</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">10</style></number><volume><style face="normal" font="default" size="100%">28</style></volume><pages><style face="normal" font="default" size="100%">121-132</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper presents a behavioural model of Trojans which formalizes the features of Trojans performance in computer systems. The Trojans behavioural model represents its life cycle including three stages: penetration, activation and executing destructive actions. Software for Trojans detection was developed. It is based on methods of detection in ‘monitor’ and ‘scanner’ modes. Trojans detection in monitor mode is based on a novel technique for computer system Trojans detection which uses fuzzy logic. It enables a conclusion about the degree of danger of infecting the computer system with Trojans. Trojans detection in a scanner mode is based on a novel technique for constructing the protected sequences and generation of detectors based on algorithms for artificial immune systems. It allows to reveal the fact of system files substitution of Trojans’ versions. Trojan detection software allows to detect new Trojans with high degree of reliability and efficiency.</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%">Georgi Kirov</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Soft Computing Agents for Dynamic Routing</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%">distributed information systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Fuzzy Logic</style></keyword><keyword><style  face="normal" font="default" size="100%">fuzzy routing.</style></keyword><keyword><style  face="normal" font="default" size="100%">soft computing agents</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2003</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2003</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">104-116</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper reviews and evaluates the state-of-the-art in Distributed Information Systems. It outlines some disadvantages of distributed software applications (world-wide information services, databases, and software packages that are connected through the Internet and other network systems). It is concluded that the field of distributed network systems is in a critical need of intuitive and innovative approaches to address the growing complexity in all of its different aspects: communication, routing, performance, stability, connectivity. In an attempt to resolve the above-mentioned problems an approach is proposed that combines the Bee-gent agent technology and the fuzzy-logic representation. The paper presents an example of soft-computing agents for dynamic routing that uses distributed database applications as illustration of the concept.</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%">Albena Tchamova</style></author><author><style face="normal" font="default" size="100%">Tzvetan Semerdjiev</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fuzzy Logic Approach to Estimating Tendencies in Target Behavior</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%">attribute data processing</style></keyword><keyword><style  face="normal" font="default" size="100%">evidence reasoning</style></keyword><keyword><style  face="normal" font="default" size="100%">Fuzzy Logic</style></keyword><keyword><style  face="normal" font="default" size="100%">Fuzzy sets</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2002</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2002</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">58-69</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In some real-world situations when kinematic data is not available or it is not sufficient to provide right decisions and/or accurate estimates, estimation schemes may incorporate the attribute data that usually exists simultaneously with kinematic data. However, attribute data is usually incomplete, inconsistent and vague, hence the importance of the problem of overcoming the arising uncertainty in such cases. This paper presents one approach to the estimation of the tendency of target behavior. The authors present an original algorithm for tracking target behavior and evaluate its performance. The algorithm is based on the application of the principles of fuzzy logic to conventional passive radar amplitude measurements. A set of fuzzy models is used to describe alternative tendencies of target behavior. Additionally, a noise reduction procedure is applied. The performance of the developed algorithm in the presence of noise is estimated based on computer simulations results.</style></abstract></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%">Todor Tagarev</style></author><author><style face="normal" font="default" size="100%">Petya Ivanova</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Computational Intelligence in Multi-Source Data and Information Fusion</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%">Computational Intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">Decision Support</style></keyword><keyword><style  face="normal" font="default" size="100%">Forecasting</style></keyword><keyword><style  face="normal" font="default" size="100%">Fuzzy Logic</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">MSDF</style></keyword><keyword><style  face="normal" font="default" size="100%">Neural Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Pattern Recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">Soft Computing</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%">2</style></volume><pages><style face="normal" font="default" size="100%">33-49</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A model of MultiSource Information Fusion (MSIF) is proposed. It expands the application of proven MSDF techniques to diverse problem areas. This model allows for a unified framework clearly distinguishing processing functions from methods dealing with partial, uncertain, and imprecise information. The concept of computational intelligence provides for a holistic approach to design and integration of methods and algorithms for information fusion. We describe the application of computational intelligence to the fusion of data and information in two studies of early warning. The emphasis is on the power of soft-computing methods in designing early warning architectures pertinent to forecasting events in complex dynamical systems. </style></abstract></record></records></xml>