<?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%">George Sharkov</style></author><author><style face="normal" font="default" size="100%">Wim Mees</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Enhanced Collaboration for Cyber Security and Resilience</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%">artificial intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">Collaborative Network Organization</style></keyword><keyword><style  face="normal" font="default" size="100%">cyber digital skills</style></keyword><keyword><style  face="normal" font="default" size="100%">cyber range</style></keyword><keyword><style  face="normal" font="default" size="100%">digital transformation</style></keyword><keyword><style  face="normal" font="default" size="100%">ECHO project</style></keyword><keyword><style  face="normal" font="default" size="100%">human factor</style></keyword><keyword><style  face="normal" font="default" size="100%">privacy</style></keyword><keyword><style  face="normal" font="default" size="100%">Situational awareness</style></keyword><keyword><style  face="normal" font="default" size="100%">threat intelligence</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><volume><style face="normal" font="default" size="100%">53</style></volume><pages><style face="normal" font="default" size="100%">7-8</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This editorial article introduces the structure and content of articles accepted for presentation at the Fourth International Scientific Conference “Digital Transformation, Cyber Security and Resilience, DIGILIENCE 2022. The volume includes articles presenting results on six particular topics: Advanced Threat Intelligence and Information Sharing; Digitalization and Privacy Preservation; Governing Cybersecurity Networks and Ecosystems; Developing Critical Cyber Skills; Human Factors for Safety and Resilience to Cyber/Hybrid Influence; and Cyber Ranges, Simulation and Training.</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><section><style face="normal" font="default" size="100%">7</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%">Dimitar Kamenov</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Intelligent Methods for Big Data Analytics and Cyber Security</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%">big data</style></keyword><keyword><style  face="normal" font="default" size="100%">classification</style></keyword><keyword><style  face="normal" font="default" size="100%">clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">cyber security</style></keyword><keyword><style  face="normal" font="default" size="100%">data mining</style></keyword><keyword><style  face="normal" font="default" size="100%">filtering</style></keyword><keyword><style  face="normal" font="default" size="100%">human factor</style></keyword><keyword><style  face="normal" font="default" size="100%">machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">network graphs</style></keyword><keyword><style  face="normal" font="default" size="100%">outlier detection</style></keyword><keyword><style  face="normal" font="default" size="100%">semantic analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">spatial data mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">statistical analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">39</style></volume><pages><style face="normal" font="default" size="100%">255-262</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The article examines some intelligent computational methods for big data analysis which are applicable to issues of cyber security and military science, including the analysis of hybrid threats. It presents and compares big data analysis techniques such as quantitative analysis, qualitative analysis, data mining, statistical analysis, machine learning, semantic analysis, and visual analysis. The importance and prospects of intelligent methods for big data analysis are emphasized.
</style></abstract><issue><style face="normal" font="default" size="100%">3</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%">Zlatogor Minchev</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Challenges to Human Factor for Advance Persistent Threats Proactive Identification in Modern Social Networks</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%">advanced persistent threats</style></keyword><keyword><style  face="normal" font="default" size="100%">cyber space</style></keyword><keyword><style  face="normal" font="default" size="100%">human factor</style></keyword><keyword><style  face="normal" font="default" size="100%">proactive identification</style></keyword><keyword><style  face="normal" font="default" size="100%">social networks</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><volume><style face="normal" font="default" size="100%">34</style></volume><pages><style face="normal" font="default" size="100%">123-136</style></pages><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The paper looks into the issue of proactive advanced persistent threats (APTs) identification in modern social networks. As these threats are quite unnoticeable and require a long-term, comprehensive monitoring of both technologies and users, a hybrid methodological framework is proposed. A combination of: experts&amp;rsquo; knowledge and beliefs, system analysis and real environment interactive validation is presented to meet practical APT challenges. The obtained results provide an explanatory foundation for a better understanding the interaction process of the human factor with future technological developments and resulting threats advances in the evolving cyber space.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><section><style face="normal" font="default" size="100%">123</style></section></record></records></xml>