<?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%">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%">Dobrin Mahlyanov</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Internet of Things – A New Attack Vector for Hybrid Threats</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%">attacks against IoT.</style></keyword><keyword><style  face="normal" font="default" size="100%">Hybrid threats</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet of Things</style></keyword><keyword><style  face="normal" font="default" size="100%">IoT</style></keyword><keyword><style  face="normal" font="default" size="100%">Security in IoT</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%">175-182</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The means of conducting hybrid warfare are rapidly changing. IoT is a recent sphere of activity, opening new opportunities for hybrid influence. Consisting of three main building blocks, IoT inherits all security problem specific to each one of them and introduces some new ones. This article describes in brief the main issues concerning security in IoT and ways of using it for creating hybrid threats. All of the described problems can be used for escalation in different areas. The article presents also a simple definition of what is a (relatively) secure IoT system and an original concept for reducing vulnerabilities in the IoT environment.
</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></record></records></xml>