<?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%">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%">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></records></xml>