Rodyti trumpą aprašą

dc.contributor.authorVitkus, Donatas
dc.contributor.authorJezukevičiūtė, Justina
dc.contributor.authorGoranin, Nikolaj
dc.date.accessioned2023-09-18T20:31:15Z
dc.date.available2023-09-18T20:31:15Z
dc.date.issued2020
dc.identifier.issn1841-9836
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/150658
dc.description.abstractFast development of information systems and technologies while providing new opportunities for people and organizations also make them more vulnerable at the same time. Information security risk assessment helps to identify weak points and preparing mitigation actions. The analysis of expert systems has shown that rule-based expert systems are universal, and because of that can be considered as a proper solution for the task of risk assessment automation. But to assess information security risks quickly and accurately, it is necessary to process a large amount of data about newly discovered vulnerabilities or threats, to reflect regional and industry specific information, making the traditional approach of knowledge base formation for expert system problematic. This work presents a novel method for an automated expert systems knowledge base formation based on the integration of data on regional malware distribution from Cyberthreat real-time map providing current information on newly discovered threats. In our work we collect the necessary information from the web sites in an automated way, that can be later used in a relevant risk calculation. This paper presents method implementation, which includes not only knowledge base formation but also the development of the prototype of an expert system. It was created using the JESS expert system shell. Information security risk evaluation was performed according to OWASP risk assessment methodology, taking into account the location of the organization and prevalent malware in that area.eng
dc.formatPDF
dc.format.extentp. 1-9
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyDBLP
dc.relation.isreferencedbyDOAJ
dc.relation.isreferencedbyScopus
dc.relation.isreferencedbyScience Citation Index Expanded (Web of Science)
dc.rightsLaisvai prieinamas internete
dc.source.urihttps://doi.org/10.15837/ijccc.2020.3.3865
dc.source.urihttp://univagora.ro/jour/index.php/ijccc/article/view/3865/1401
dc.source.urihttps://talpykla.elaba.lt/elaba-fedora/objects/elaba:68288874/datastreams/MAIN/content
dc.titleDynamic expert system-based geographically adapted malware risk evaluation method
dc.typeStraipsnis Web of Science DB / Article in Web of Science DB
dcterms.accessRightsThis is an open access article distributed under the terms and conditions of the Creative CommonsAttribution-NonCommercial 4.0 International License.
dcterms.licenseCreative Commons – Attribution – 4.0 International
dcterms.references21
dc.type.pubtypeS1 - Straipsnis Web of Science DB / Web of Science DB article
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.facultyFundamentinių mokslų fakultetas / Faculty of Fundamental Sciences
dc.subject.researchfieldT 007 - Informatikos inžinerija / Informatics engineering
dc.subject.vgtuprioritizedfieldsIK0101 - Informacijos ir informacinių technologijų sauga / Information and Information Technologies Security
dc.subject.ltspecializationsL106 - Transportas, logistika ir informacinės ir ryšių technologijos (IRT) / Transport, logistic and information and communication technologies
dc.subject.eninformation security risk analysis
dc.subject.enexpert systems
dc.subject.enknowledge base formation
dc.subject.enJESS
dc.subject.eninformation acquisition
dcterms.sourcetitleInternational journal of computers communications & control
dc.description.issueiss. 3
dc.description.volumevol. 15
dc.publisher.nameAgora University
dc.publisher.cityOradea
dc.identifier.doi000528258600008
dc.identifier.doi10.15837/ijccc.2020.3.3865
dc.identifier.elaba68288874


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