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dc.contributor.authorHannay, Jo Erskine
dc.contributor.authorStolpe, Audun
dc.contributor.authorYamin, Muhammad Mudassar
dc.date.accessioned2022-02-16T11:23:46Z
dc.date.available2022-02-16T11:23:46Z
dc.date.created2021-11-26T16:30:09Z
dc.date.issued2021
dc.identifier.isbn978-3-030-90963-5
dc.identifier.urihttps://hdl.handle.net/11250/2979324
dc.description.abstractThere is an immediate need for a greater number of highly skilled cybersecurity personnel to meet intensified cyber attacks. We propose a cyber range exercise management architecture that employs machine reasoning to structure the design, execution and analysis of cyber range training scenarios. The scenarios are then used in simulation-based training in an emulated IT infrastructure environment. The machine reasoning is obtained by combining four AI methods: attack-defence trees, formal argumentation theory, answer set programming and multiagent systems. We argue that this type of advanced functionality that supports exercise managers in their design and analysis of scenarios is strictly necessary to improve current exercise management systems and build the required cybersecurity expertise.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.ispartofHCI International 2021 - Late Breaking Papers: Multimodality, eXtended Reality, and Artificial Intelligence 23rd HCI International Conference, HCII 2021, Virtual Event, July 24–29, 2021, Proceedings
dc.titleToward AI-Based Scenario Management for Cyber Range Trainingen_US
dc.typeChapteren_US
dc.description.versionacceptedVersionen_US
dc.rights.holderThis is the authors' accepted manuscript to an article published by Springer.en_US
dc.source.pagenumber423-436en_US
dc.identifier.doi10.1007/978-3-030-90963-5_32
dc.identifier.cristin1959960
dc.relation.projectNorges forskningsråd: 329062en_US
cristin.ispublishedtrue
cristin.fulltextpreprint
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cristin.qualitycode1


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