<?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></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><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></contributors><titles><title><style face="normal" font="default" size="100%">One-way Function Based on Modified Cellular Automata in the Diffie-Hellman Algorithm for Big Data Exchange Tasks through Open Space</style></title><secondary-title><style face="normal" font="default" size="100%">Information &amp; Security: An International Journal</style></secondary-title></titles><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%">233-246</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The article deals with ways to quickly change passwords in information ex-change through open space. It suggests an improvement of the Diffie-Hellman algorithm by creating a one-way function on the basis of cellular automata with an extended set of rules. The authors have expanded the rules of the game of life towards definition of the rules of birth rate and life extension, control of the radius of intra-population interaction, rules of death from the age of cells, multi-component (multi-population) system of cells. The created algorithm on the basis of a cellular automaton is used to create keys for safe information transfer. Depending on the needs of encryption, the algorithm can be enhanced by using variable parameters of the cell field and cell behaviour, which will allow to regulate the speed and reliability of encryption. The implementation is in Python and MatLab, which allows to compare results and change the modelling environment when changing the features of the task.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><section><style face="normal" font="default" size="100%">233</style></section></record></records></xml>