<?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%">George Sharkov</style></author><author><style face="normal" font="default" size="100%">Yavor Papazov</style></author><author><style face="normal" font="default" size="100%">Christina Todorova</style></author><author><style face="normal" font="default" size="100%">Georgi Koykov</style></author><author><style face="normal" font="default" size="100%">Georgi Zahariev</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MonSys: A Scalable Platform for Monitoring Digital Services Availability, Threat Intelligence and Cyber Resilience Situational Awareness</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%">cyber risk</style></keyword><keyword><style  face="normal" font="default" size="100%">cyber threat</style></keyword><keyword><style  face="normal" font="default" size="100%">early warning</style></keyword><keyword><style  face="normal" font="default" size="100%">resilience</style></keyword><keyword><style  face="normal" font="default" size="100%">scalability</style></keyword><keyword><style  face="normal" font="default" size="100%">Situational awareness</style></keyword><keyword><style  face="normal" font="default" size="100%">vulnerability 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%">155-167</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Today’s digital society implies interconnectivity between the online operations of different sectors of everyday life and economy alike. As a consequence, malicious activities targeted towards a single online service could hurt entire indus¬tries and multiple private and public organizations. This interdependence be¬tween online services and economic units is an imperative for targeted efforts ensuring the integrity and availability of individual systems and complex systems-of-systems alike. This article presents MonSys, a flexible, robust, and scalable monitoring platform, implement-ed as a cloud-based service and an on-premise solution, specifically de-signed to ad¬dress the need for ensuring service availability at an individual level. MonSys provides several standardized services availability checks, such as web-based services from multiple geographical locations, and a flexible platform and tools for defining customized complex services. Particular attention is paid to the processes of metrics collection, processing, storage, and querying. MonSys can perform custom availability checks for different types of infrastructures, such as various black-box, grey-box, and white-box availability checks/metrics. The article presents also results from piloting the platform on performance and scalability and options for integration in early-warning and intelligent signaling, based on behavioral pattern analysis and predictive simulations.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><section><style face="normal" font="default" size="100%">155</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%">Qiuju You</style></author><author><style face="normal" font="default" size="100%">Wei Zhu</style></author><author><style face="normal" font="default" size="100%">Jianchun Zheng</style></author><author><style face="normal" font="default" size="100%">Shaohu Tang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Vulnerability Analysis for Urban Natural Gas Pipeline Network System</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%">importance</style></keyword><keyword><style  face="normal" font="default" size="100%">inherent structural threats</style></keyword><keyword><style  face="normal" font="default" size="100%">Natural gas pipeline network</style></keyword><keyword><style  face="normal" font="default" size="100%">vulnerability 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%">40</style></volume><pages><style face="normal" font="default" size="100%">11-28</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In order to identify the vulnerable links in urban natural gas pipeline network systems, this study established a concept for the vulnerability analysis of the network system, providing a basis for quantitative analysis of vulnerability. The criteria for selecting nodes in the network were determined based on the network composition. Based on the theory of disaster chain, the vulnerability factors were analyzed thoroughly, the hazard factors causing vulnerability were determined and the vulnerable parts of the network system were identified. A model for the calculation of the structural threats from the network itself was established. The first step is to identify the interdicted point of single pipeline sections through the calculation method for friction resistance loss, and the second step is to determine the key nodes with the maximum or minimum vulnerability of the entire network, thereby realizing the point-to-net analysis of the pipeline network. The FIM model was implemented, combining the geological information system ArcGIS, Java programming language, and Lingo optimization software. Using a natural gas pipeline network in Beijing as a case study, the distribution of vulnerable points in the network was plotted and the key nodes with high vulnerability were identified by analyses of vulnerability and importance.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record></records></xml>