Cybersecurity 2030: The Synergy between Machine Learning and Generative AI
| dc.contributor.author | Silva, Mário Marques da | |
| dc.date.accessioned | 2026-07-16T11:21:14Z | |
| dc.date.available | 2026-07-16T11:21:14Z | |
| dc.date.embargo | 2035-01-01 | |
| dc.date.issued | 2025-09-19 | en_US |
| dc.date.updated | 2025-05-01T10:38:18Z | |
| dc.description.abstract | The 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.version | 6F1A-06CB-E82D | Mário Pedro Guerreiro Marques da Silva | |
| dc.description.version | info:eu-repo/semantics/acceptedVersion | |
| dc.identifier.slug | cv-prod-4481971 | |
| dc.identifier.uri | https://hdl.handle.net/11144/8005 | |
| dc.language.iso | eng | |
| dc.rights | https://purl.org/coar/access_right/c_f1cf | en_US |
| dc.subject | Cybersecurity and Supervised Learning | |
| dc.subject | Generative AI | |
| dc.subject | Machine Learning | |
| dc.subject | Hybrid Defense Strategy | |
| dc.subject | Anomaly Detection | |
| dc.subject | Synthetic Data | |
| dc.title | Cybersecurity 2030: The Synergy between Machine Learning and Generative AI | en_US |
| dc.type | conferenceObject | en_US |
| oaire.citation.title | 22nd International Conference on the Ethical and Social Impacts of ICT (Ethicomp 2025) | en_US |
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