Theory and Practice of Cybersecurity Knowledge Graphs and Further Steps

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14610%2F24%3A00136621" target="_blank" >RIV/00216224:14610/24:00136621 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.ares-conference.eu/persons/martin-hus%C3%A1k" target="_blank" >https://www.ares-conference.eu/persons/martin-hus%C3%A1k</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Theory and Practice of Cybersecurity Knowledge Graphs and Further Steps

Popis výsledku v původním jazyce

The keynote surveys the growing adoption of knowledge graphs in cybersecurity and explores their potential in cybersecurity research and practice. By structuring and interlinking vast amounts of cybersecurity data, knowledge graphs offer increasing capabilities for incident response and cyber situational awareness. They enable a holistic view of the protected cyber infrastructures and threat landscapes, facilitating advanced analytics, automated reasoning, vulnerability management, and attack mitigation. We expect the cybersecurity knowledge graphs to assist incident handlers in day-to-day cybersecurity operations as well as strategic network security management. We may see emerging tools for decision support based on knowledge graphs that will leverage continuous data collection. A knowledge graph filled with the right data at the right time can significantly reduce the workload of incident handlers. We may even see rapid changes in incident handling tools and workflows leveraging the knowledge graphs, especially when combined with emerging technologies of generative AI and large language models that will facilitate interactions with the knowledge bases or generate reports of security situations. However, the implementation of cybersecurity knowledge graphs is challenging. Ensuring the quality of the underlying data is a serious concern for researchers and practitioners. Only accurate, complete, and updated data can ensure the reliability of the knowledge graph, leading to good insights and decisions. Additionally, the dynamic nature of cyber threats necessitates continuous data updates and rigorous validation processes.

Název v anglickém jazyce

Theory and Practice of Cybersecurity Knowledge Graphs and Further Steps

Popis výsledku anglicky

The keynote surveys the growing adoption of knowledge graphs in cybersecurity and explores their potential in cybersecurity research and practice. By structuring and interlinking vast amounts of cybersecurity data, knowledge graphs offer increasing capabilities for incident response and cyber situational awareness. They enable a holistic view of the protected cyber infrastructures and threat landscapes, facilitating advanced analytics, automated reasoning, vulnerability management, and attack mitigation. We expect the cybersecurity knowledge graphs to assist incident handlers in day-to-day cybersecurity operations as well as strategic network security management. We may see emerging tools for decision support based on knowledge graphs that will leverage continuous data collection. A knowledge graph filled with the right data at the right time can significantly reduce the workload of incident handlers. We may even see rapid changes in incident handling tools and workflows leveraging the knowledge graphs, especially when combined with emerging technologies of generative AI and large language models that will facilitate interactions with the knowledge bases or generate reports of security situations. However, the implementation of cybersecurity knowledge graphs is challenging. Ensuring the quality of the underlying data is a serious concern for researchers and practitioners. Only accurate, complete, and updated data can ensure the reliability of the knowledge graph, leading to good insights and decisions. Additionally, the dynamic nature of cyber threats necessitates continuous data updates and rigorous validation processes.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/EH22_010%2F0003229" target="_blank" >EH22_010/0003229: MSCAfellow5_MUNI</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů