<?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%">Olena Davlikanova</style></author><author><style face="normal" font="default" size="100%">Larysa Kompantseva</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Political Analysis or Fortune-Telling by Crystal-Ball? Western Think Tanks' Challenges with Forecasting Putin's War</style></title><secondary-title><style face="normal" font="default" size="100%">Connections: The Quarterly 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%">conflict</style></keyword><keyword><style  face="normal" font="default" size="100%">content analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">escalation</style></keyword><keyword><style  face="normal" font="default" size="100%">full-scale invasion</style></keyword><keyword><style  face="normal" font="default" size="100%">political analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Russia</style></keyword><keyword><style  face="normal" font="default" size="100%">Ukraine</style></keyword><keyword><style  face="normal" font="default" size="100%">war</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2022</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">9-28</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This article analyzes major western think tanks’ forecasts, experts’ opinions, and US and UK media content regarding the future of Ukraine-Russia relationships in the year preceding Russia’s full-scale invasion of Ukraine on February 24, 2022. Though the Russian-Ukrainian war has been ongoing since the occupation of Crimea and quasi-republics (“Donetsk and Luhansk People’s Republics”) were established in 2014, not many political analysts foresaw the coming of the bloodiest and most devastating war since WWII. At the same time, Big Data content analysis of US and UK media demonstrated the presence of markers of an approaching full-scale invasion. Correct-based estimation of the likelihood of a Russian invasion of Ukraine, as well as Ukraine’s willingness and ability to protect its sovereignty, was crucial for shaping the appropriate response of the Collective West.</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><section><style face="normal" font="default" size="100%">9</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mubarak Albarka Umar</style></author><author><style face="normal" font="default" size="100%">Zhanfang Chen</style></author><author><style face="normal" font="default" size="100%">Yan Liu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Hybrid Intrusion Detection with Decision Tree for Feature Selection</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%">feature selection</style></keyword><keyword><style  face="normal" font="default" size="100%">hybrid IDS</style></keyword><keyword><style  face="normal" font="default" size="100%">IDS dataset</style></keyword><keyword><style  face="normal" font="default" size="100%">intrusion detection</style></keyword><keyword><style  face="normal" font="default" size="100%">machine learning algorithms</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><volume><style face="normal" font="default" size="100%">49</style></volume><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;margin-left:19.85pt;&quot;&gt;Intrusion detection systems (IDS) typically take high computational complexity to examine data features and identify intrusion patterns due to the size and nature of the current intrusion detection datasets. Data pre-processing techniques (such as feature selection) are being used to reduce such complexity by eliminating irrelevant and redundant features in such datasets. The objective of this study is to analyse the effectiveness and efficiency of some feature selection approaches, namely wrapper-based and filter-based modelling approaches. To achieve that, machine learning models are designed in a hybrid approach with either wrapper or filter selection processes. Five machine learning algorithms are used on the wrapper and filter-based feature selection methods to build the IDS models using the UNSW-NB15 dataset. The wrapper-based hybrid intrusion detection model comprises a decision tree algorithm to guide the selection process and three filter-based methods, namely information gain, gain ratio, and relief, are used for comparison to determine the efficiency and effectiveness of the wrapper approach. Furthermore, a comparison with other state-of-the-art intrusion detection approaches is performed. The experimental results show that the wrapper-based method is quite effective in comparison to state-of-the-art works; however, it requires high computational time in comparison to the filter-based methods while achieving similar results. Our work also revealed unobserved issues on the conformity of the UNSW-NB15 dataset.&lt;/p&gt;</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%">George Sharkov</style></author><author><style face="normal" font="default" size="100%">Andon Lazarov</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cyber Protection of Critical Infrastructures, Novel Big Data and Artificial Intelligence Solutions</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%">big data</style></keyword><keyword><style  face="normal" font="default" size="100%">Critical Infrastructure Protection</style></keyword><keyword><style  face="normal" font="default" size="100%">Cybersecurity</style></keyword><keyword><style  face="normal" font="default" size="100%">ICT security</style></keyword><keyword><style  face="normal" font="default" size="100%">IoT</style></keyword><keyword><style  face="normal" font="default" size="100%">machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">resilience</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%">47</style></volume><pages><style face="normal" font="default" size="100%">7-10</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 reader to the Second International Scientific Conference “Digital Transformation, Cyber Security and Resilience,” DIGILIENCE 2020, and summarises the results from five of its sessions: Protecting Critical Infrastructures from Cyberattacks; Security Implications and Solutions for IoT Systems; Big Data and Artificial Intelligence for Cybersecurity; Secure Communication and Information Protection; and Advanced ICT Security Solutions.</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%">Peter Lenk</style></author><author><style face="normal" font="default" size="100%">Michael Street</style></author><author><style face="normal" font="default" size="100%">Ivana Ilic Mestric</style></author><author><style face="normal" font="default" size="100%">Arvid Kok</style></author><author><style face="normal" font="default" size="100%">Giavid Valiyev</style></author><author><style face="normal" font="default" size="100%">Philippe Le Cerf</style></author><author><style face="normal" font="default" size="100%">Barbara Lorincz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data Science as a Service: The Data Range</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%">big data</style></keyword><keyword><style  face="normal" font="default" size="100%">charge back</style></keyword><keyword><style  face="normal" font="default" size="100%">data engineer</style></keyword><keyword><style  face="normal" font="default" size="100%">data engineering</style></keyword><keyword><style  face="normal" font="default" size="100%">data range</style></keyword><keyword><style  face="normal" font="default" size="100%">data science</style></keyword><keyword><style  face="normal" font="default" size="100%">data science as a service</style></keyword><keyword><style  face="normal" font="default" size="100%">machine learning</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%">47</style></volume><pages><style face="normal" font="default" size="100%">157-171</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">As with many new disciplines, in many organisations data science is being embraced in a piecemeal way with many parts of organisations creating special purpose environments designed to answer specific problems, fragmenting the overall capacity and knowledge base. Often vendors selling proprietary approaches, potentially creating lock-in, fuel these isolated solutions. This article’s main contribution is a ‘Data Science as a Service (DSaaS)’ model, where common elements required to conduct data science are abstracted and gathered into a logical layered, service-based architecture. This way, each element of the organisation can utilise the services it needs to progress its work, use specific solutions or share common tool sets, share results in a ‘model zoo,’ share data sets, share best practices and benefit from common, robust high-performance infrastructure and tools. With such an approach, it is possible to cluster data science skill sets and provide critical mass where needed. The proposed approach also facilitates a charge-back business model, where data science services are costed and charged to internal organisational elements or external customers in a measured, pay-as-you go way.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><section><style face="normal" font="default" size="100%">157</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%">Volodymyr Petrivskyi</style></author><author><style face="normal" font="default" size="100%">Georgi Dimitrov</style></author><author><style face="normal" font="default" size="100%">Viktor Shevchenko</style></author><author><style face="normal" font="default" size="100%">Oleksiy Bychkov</style></author><author><style face="normal" font="default" size="100%">Magdalena Garvanova</style></author><author><style face="normal" font="default" size="100%">Galina Panayotova</style></author><author><style face="normal" font="default" size="100%">Pavel Petrov</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Information Technology for Big Data Sensor Networks Stability Estimation</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%">coverage radius</style></keyword><keyword><style  face="normal" font="default" size="100%">data loss</style></keyword><keyword><style  face="normal" font="default" size="100%">sensor network</style></keyword><keyword><style  face="normal" font="default" size="100%">sensor network stability</style></keyword><keyword><style  face="normal" font="default" size="100%">sensors</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%">47</style></volume><pages><style face="normal" font="default" size="100%">141-154</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Sensors and sensor networks are widely used in various industries and human activities and main components of the Internet of Things and Industrial Internet of Things concepts. Nowadays, huge amounts of data, called Big Data, can be transported and collected using sensor networks. This article presents approaches for providing sensor networks’ stability estimation for transporting Big Data and collecting respective use cases. The proposed estimation method allows to detect sensor network’s components that decrease data transport system stability. Consequently, additional connections or sensors can be added to increase stability. In the case of data collection, the solution consists of finding the most vulnerable sensor and an optimal position for the additional sensor with given intersection levels with other sensors. Simulation results confirm the feasibility and effectiveness of the proposed approaches.</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><section><style face="normal" font="default" size="100%">141</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%">Dmytro Lande</style></author><author><style face="normal" font="default" size="100%">Igor Subach</style></author><author><style face="normal" font="default" size="100%">Alexander Puchkov</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A System for Analysis of Big Data from Social Media</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%">Cyber Aggregator</style></keyword><keyword><style  face="normal" font="default" size="100%">cyber security</style></keyword><keyword><style  face="normal" font="default" size="100%">OSINT</style></keyword><keyword><style  face="normal" font="default" size="100%">social media monitoring</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%">47</style></volume><pages><style face="normal" font="default" size="100%">44-61</style></pages><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The article presents the basic principles of building and using a monitoring and analysis system of social media on cybersecurity, based on the concepts of Big Data, Data/Text Mining, Information Extraction, Complex Networks. The authors substantiate information technologies for creating a system of content monitoring, selection of relevant information from social networks, implementation of search engines for their refinement by users, saving queries as RSS feeds, and maintaining personal databases in client applications.&lt;/p&gt;&lt;p&gt;The described OSINT system is based on collection of information from open sources, its analysis, preparation and timely delivery of the final product to the customer in order to solve certain intelligence tasks. Hence, the system is the result of a systematic collection, processing and analysis of the necessary publicly available information. It is based on the application of methods and tools of information retrieval, data analysis and aggregation of information flows, and is used for social media content monitoring as a component of decision support systems for information and cybersecurity.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><section><style face="normal" font="default" size="100%">44</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%">Alfredo M. Ronchi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">TAS: Trust Assessment System</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%">border security</style></keyword><keyword><style  face="normal" font="default" size="100%">human factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Risk Assessment</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%">44</style></volume><pages><style face="normal" font="default" size="100%">62-75</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The present article briefly introduces a general view on tomorrow’s border control system and EU inter-BCP real time information sharing, exploring and proposing new operational methods and solutions for border control procedures to increase the efficacy and efficiency of the whole security screening system at the same time reducing the efforts (costs/resources). The general description of the system logic and architecture introduces the core of the solution, the Trust Assessment System. A “black box” based on risk analysis and advanced machine learning algorithms aimed to assign a Traveller Trust Score to each single individual intending to cross the border. Main benefits are: improved checkpoint throughput, improved situational awareness and level of security, better traveller experience, optimisation of resources. The concept is that the traveller risk evaluation starts as soon as she/he applies for a visa, a passport or books a trip by whatever means of transport.</style></abstract><section><style face="normal" font="default" size="100%">62</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.
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