<?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%">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%">Michael Street</style></author><author><style face="normal" font="default" size="100%">Peter Lenk</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Aspect Level Sentiment Analysis Methods Applied to Text in Formal Military Reports</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%">aspect-based sentiment analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">co-reference resolution</style></keyword><keyword><style  face="normal" font="default" size="100%">dependency parsing</style></keyword><keyword><style  face="normal" font="default" size="100%">natural language processing</style></keyword><keyword><style  face="normal" font="default" size="100%">NLP</style></keyword><keyword><style  face="normal" font="default" size="100%">rule-base sentiment analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">sentiment analysis</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%">227-238</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Many military functions such as intelligence collection or lessons learned analysis demand an understanding of situations derived from large quantities of written material. This paper describes approaches to gain greater understanding of document content by applying rule-based approaches in addition to open source machine learning models. The performance of two approaches to sentiment analysis are assessed, when operating on document sets from NATO sources. This combination enables analysts to identify items of interest within large document sets more effectively, by indicating the sentiment around specific aspects (nouns) which refer to a specific target (noun) in the text. This enables data science to give users a more detailed understanding of the content of large quantities of documents with respect to a particular target or subject.</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><section><style face="normal" font="default" size="100%">227</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>47</ref-type><contributors><authors><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%">Michael Street</style></author><author><style face="normal" font="default" size="100%">Peter Lenk</style></author><author><style face="normal" font="default" size="100%">Mihaela Racovita</style></author><author><style face="normal" font="default" size="100%">Filipe Vieira</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Extracting Value from NATO Data Sets through Machine Learning and Advanced Data Analytics</style></title><secondary-title><style face="normal" font="default" size="100%">IST-178 specialists meeting on Big data challenges: situational awareness and decision support</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">October 2019</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Budapest</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><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%">Michael Street</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Technical Report: Exploration of NATO Exercise Big Data for Lessons Learned</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">December 2019</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">NCI Agency</style></publisher><pub-location><style face="normal" font="default" size="100%">The Hague</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>