Cybersecurity 2030: The Synergy between Machine Learning and Generative AI

dc.contributor.authorSilva, Mário Marques da
dc.date.accessioned2026-07-16T11:21:14Z
dc.date.available2026-07-16T11:21:14Z
dc.date.embargo2035-01-01
dc.date.issued2025-09-19en_US
dc.date.updated2025-05-01T10:38:18Z
dc.description.abstractThe rapid evolution of technology, characterized by the 4th Industrial Revolution, has reshaped the cybersecurity landscape. This paper explores the implementation of Generative AI in the context of cybersecurity, highlighting their applications in data analysis procedures and automatic control systems. By integrating machine learning (ML) for real-time detection and generative AI for simulating advanced attack scenarios, we can detect cyberattacks at an early stage and minimize the impact on the systems functioning. We conclude by discussing the implications for the future of cybersecurity and the anticipated dominance of AI-driven solutions by 2030.
dc.description.version6F1A-06CB-E82D | Mário Pedro Guerreiro Marques da Silva
dc.description.versioninfo:eu-repo/semantics/acceptedVersion
dc.identifier.slugcv-prod-4481971
dc.identifier.urihttps://hdl.handle.net/11144/8005
dc.language.isoeng
dc.rightshttps://purl.org/coar/access_right/c_f1cfen_US
dc.subjectCybersecurity and Supervised Learning
dc.subjectGenerative AI
dc.subjectMachine Learning
dc.subjectHybrid Defense Strategy
dc.subjectAnomaly Detection
dc.subjectSynthetic Data
dc.titleCybersecurity 2030: The Synergy between Machine Learning and Generative AIen_US
dc.typeconferenceObjecten_US
oaire.citation.title22nd International Conference on the Ethical and Social Impacts of ICT (Ethicomp 2025)en_US

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