<?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%">Alfredo M. Ronchi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Human Factor, Resilience, and Cyber/Hybrid Influence</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%">cyberwarfare</style></keyword><keyword><style  face="normal" font="default" size="100%">Hybrid threats</style></keyword><keyword><style  face="normal" font="default" size="100%">machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">soft concerns</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%">221-239</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The aim of this article is to depict some of the impacts of the ongoing digital transition on security, considering human factors, resilience, cyber and hybrid threats. After a short description of literature and related works, the article focuses on the term “security” to better clarify the meaning, then introduces the process of digital transition and related aspects including “datafication” and potential harms to cybersecurity and the potential resilience breaches due to the concentration of tasks based on digital technology including production chains and digital manufacturing.
Recently, the digital transformation has had a considerable impact on cybersecurity due to the boost generated by the pandemic and the increasing number of “digitally divided” citizens forced to “go digital” and related need to foster a culture of cybersecurity since the primary schools. This section includes an overview of different approaches to the “securitisation” of cyberspace. Back to security in a broad sense freedom of expression is the first aspect considered, including hate, fake news and propaganda, influence on opinion dynamics potentially applicable to the social and political sectors, as a kind of technological extension the combined use of big data and machine learning to activate nudging as a silent weapon, the risks directly connected to the concentration in few countries of online platforms directly connected with the last topic that is the emerging Internet of behaviour that thanks to the incredible amount of users’ data can monitor ad address citizens’ behaviours.
The list of impacts included will simply provide an idea about some of the potential threats, but they are not limited to this set.
</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><section><style face="normal" font="default" size="100%">221</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></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%">Maksym Brazhenenko</style></author><author><style face="normal" font="default" size="100%">Victor Shevchenko</style></author><author><style face="normal" font="default" size="100%">Oleksiy Bychkov</style></author><author><style face="normal" font="default" size="100%">Boyan Jekov</style></author><author><style face="normal" font="default" size="100%">Pepa Petrova</style></author><author><style face="normal" font="default" size="100%">Eugenia Kovatcheva</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Adopting Machine Learning for Images Transferred with LoRaWAN</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%">cloud</style></keyword><keyword><style  face="normal" font="default" size="100%">IoT</style></keyword><keyword><style  face="normal" font="default" size="100%">LoRa</style></keyword><keyword><style  face="normal" font="default" size="100%">machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Raspberry PI</style></keyword><keyword><style  face="normal" font="default" size="100%">security</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%">172-186</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">LPWAN (Low-power Wide-area network) networks are well-known since the 1980s, but due to low efficiency were not in active use for a long time. Modern LPWAN is a game-changing technology with true power in net-work coverage, cost efficiency, and low operational expenses. LPWAN services’ most frequent market is in smart cities, agriculture, healthcare, and civil defence systems. LoRa is considered one of the market leaders in LPWAN; however, the low bandwidth of its physical layer makes it unsuitable for high-speed transmission. The provision of integrity, availability, and confidentiality in IoT networks is still of major concern. Data accuracy and lack of control over the transmission of personal information prevents the active use of the technology in regulated industries, such as healthcare and civil defence. In this article, we adopt LoRa for the trans-mission of media content, with an ability to regulate the quality of data and achieve desired level of integrity and availability. This allows the system to self-configure (train) via more reliable machine learning techniques.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><section><style face="normal" font="default" size="100%">172</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%">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%">Arvid Kok</style></author><author><style face="normal" font="default" size="100%">Ivana Ilic Mestric</style></author><author><style face="normal" font="default" size="100%">Giavid Valiyev</style></author><author><style face="normal" font="default" size="100%">Michael Street</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cyber Threat Prediction with Machine Learning</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%">auto-encoding</style></keyword><keyword><style  face="normal" font="default" size="100%">clustering with outliers</style></keyword><keyword><style  face="normal" font="default" size="100%">Cybersecurity</style></keyword><keyword><style  face="normal" font="default" size="100%">DBSCAN</style></keyword><keyword><style  face="normal" font="default" size="100%">deep learning</style></keyword><keyword><style  face="normal" font="default" size="100%">KNIME Analytics Platform</style></keyword><keyword><style  face="normal" font="default" size="100%">machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">MITRE ATT@CK framework</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%">203-220</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper we address the approaches, techniques and results of applying machine learning techniques for cyber threat prediction. Timely discovery of advanced persistent threats is of utmost importance for the protection of NATO’s and its allies’ networks. Therefore, NATO and NATO Communication and Information Agency’s Cyber Security service line is constantly looking for improvements. During Coalition Warrior Interoperability Exercise (CWIX) event data was captured on a Red-Blue Team Simulation. The data set was then used to apply a variety of Machine Learning techniques: deep-learning, auto-encoding and clustering with outliers.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><section><style face="normal" font="default" size="100%">203</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%">Giavid Valiyev</style></author><author><style face="normal" font="default" size="100%">Marcello Piraino</style></author><author><style face="normal" font="default" size="100%">Arvid Kok</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%">Retzius Birger</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Initial Exploitation of Natural Language Processing Techniques on NATO Strategy and Policies</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%">data science</style></keyword><keyword><style  face="normal" font="default" size="100%">machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">NLP</style></keyword><keyword><style  face="normal" font="default" size="100%">semantic similarity search</style></keyword><keyword><style  face="normal" font="default" size="100%">text similarity</style></keyword><keyword><style  face="normal" font="default" size="100%">thesaurus</style></keyword><keyword><style  face="normal" font="default" size="100%">triples</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%">187-202</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper describes initial exploitation of Natural Language Processing (NLP) techniques applied to a specific set of related NATO documents. In particular, the text similarity technique was applied to document sets with the aim of capturing the relationships between documents or sections of documents from semantic and syntactic perspectives. Thesaurus and triple extraction techniques allowed the understanding of the sentences beyond the syntactic structure, thus improving the accuracy in capturing similar content across documents with diverse syntactic structures. The objective is to assess whether Natural Language Processing tools can retrieve relationships and gaps between such kinds of textual data. This work improves interoperability in NATO by enhancing the development and application of policies, directives and other documents, which dictate how Consultation, Command and Control (C3) systems across the Alliance interoperate and support NATO's operational needs.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><section><style face="normal" font="default" size="100%">187</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%">Tuomo Sipola</style></author><author><style face="normal" font="default" size="100%">Samir Puuska</style></author><author><style face="normal" font="default" size="100%">Tero Kokkonen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Model Fooling Attacks Against Medical Imaging: A Short Survey</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%">adversarial images</style></keyword><keyword><style  face="normal" font="default" size="100%">artificial neural networks</style></keyword><keyword><style  face="normal" font="default" size="100%">deep learning</style></keyword><keyword><style  face="normal" font="default" size="100%">machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">medical imaging</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%">215-224</style></pages><abstract><style face="normal" font="default" size="100%">This study aims to find a list of methods to fool artificial neural networks used in medical imaging. We collected a short list of publications related to machine learning model fooling to see if these methods have been used in the medical imaging domain. Specifically, we focused our interest to pathological whole slide images used to study human tissues. While useful, machine learning models such as deep neural networks can be fooled by quite simple attacks involving purposefully engineered images. Such attacks pose a threat to many domains, including the one we focused on since there have been some studies describing such threats.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><section><style face="normal" font="default" size="100%">215</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%">Michał Bajor</style></author><author><style face="normal" font="default" size="100%">Marcin Niemiec</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A New Steganographic Algorithm for Hiding Messages in Music</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%">audio</style></keyword><keyword><style  face="normal" font="default" size="100%">machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">MIDI format</style></keyword><keyword><style  face="normal" font="default" size="100%">steganography</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%">261-275</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Steganography in audio files usually revolves around well-known concepts and algorithms, least significant bit algorithm to name one. This paper proposes a new, alternative approach where steganographic information is connected with the medium even more – by using the medium itself as the information. The goal of this paper is to present a new aspect of steganography, which utilizes machine learning. This form of steganography may produce statistically indeterminable steganographic files which are immune to brute force attempts at trying to retrieve the hidden messages. Then the proposed solution is verified against statistical analysis and brute force attacks with promising results.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><section><style face="normal" font="default" size="100%">261</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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