Adversary Tactic Driven Scenario and Terrain Generation with Partial Infrastructure Specification

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F24%3A00136100" target="_blank" >RIV/00216224:14330/24:00136100 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1145/3664476.3664523" target="_blank" >http://dx.doi.org/10.1145/3664476.3664523</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3664476.3664523" target="_blank" >10.1145/3664476.3664523</a>

Result language
angličtina
Original language name
Adversary Tactic Driven Scenario and Terrain Generation with Partial Infrastructure Specification
Original language description
Diverse, accurate, and up-to-date training environments are essential for training cybersecurity experts and autonomous systems. However, preparation of their content is time-consuming and requires experts to provide detailed specifications. In this paper, we explore the challenges of automated generation of the content (composed of scenarios and terrains) for these environments. We propose new models to represent the cybersecurity domain and associated action spaces. These models are used to create sound and complex training content based on partial specifications provided by users. We compare the results with a real-world complex malware campaign to assess the realism of the synthesized content. To further evaluate the correctness and variability of the results, we utilize the kill-chain attack graph generation for the generated training content to asses the internal correspondence of its key components. Our results demonstrate that the proposed approach can create complex training content similar to advanced attack campaigns, which passes evaluation for soundness and practicality. Our proposed approach and its implementation significantly contribute to the state of the art, enabling novel approaches to cybersecurity training and autonomous system development.
Czech name
—
Czech description
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Project
<a href="/en/project/VJ02010020" target="_blank" >VJ02010020: AI-Dojo: Multiagent Testbed for Research and Testing of AI-driven Cybersecurity Technologies</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
ARES '24: Proceedings of the 19th International Conference on Availability, Reliability and Security
ISBN
9798400717185
ISSN
—
e-ISSN
—
Number of pages
11
Pages from-to
1-11
Publisher name
Association for Computing Machinery
Place of publication
New York, United States
Event location
Vienna
Event date
Jul 30, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001283894700045

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