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Next‐Generation Network Management: Harnessing AI to Automate Operations

2025-10-31Master thesis Embargoed access
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datacite.subject.fosEngenharia e Tecnologia::Outras Engenharias e Tecnologias
dc.contributor.advisorFuentes, Daniel Alexandre Lopes
dc.contributor.advisorFrazão, Luís Alexandre Lopes
dc.contributor.advisorCorreia, Luís Filipe Jesus
dc.contributor.advisorCosta, Nuno Alexandre Ribeiro da
dc.contributor.advisorPereira, António Manuel de Jesus
dc.contributor.authorVieira, Gabriel Madeira
dc.date.accessioned2025-11-26T09:57:22Z
dc.date.available2025-11-26T09:57:22Z
dc.date.issued2025-10-31
dc.description.abstractCybersecurity infrastructures face constant challenges, including increasingly sophisticated threats, the rising costs of Security Operations Centres (SOCs), and a growing shortage of skilled professionals. To address these issues, this dissertation proposes an AI-based architectural framework designed to automate network security and enhance threat mitigation. The proposed framework integrates Software-Defined Networking (SDN) and Security Information and Event Management (SIEM) with AI-driven Intrusion Detection and Prevention Systems (IDS/IPS). It incorporates a lightweight Large Language Model (LLM) under 4GB, trained on MikroTik documentation to translate user intent into network commands. In addition, several machine learning models are trained and evaluated for real-time threat detection, supported by a digital twin and a sandbox for configuration testing. Three specialised datasets from scraped documentation and available APIs—pretraining, QA, and reasoning—were developed, totalling 74,482 records. A web interface and REST APIs provide accessibility. Experimental results show that the AI models achieve a 74% LLM generated command execution success rate, substantially surpassing the 8% untrained baseline, and the selected machine learning classifier attains a 94.84% F1-score for threat detection, thereby supporting the validity of the proposed approach. This proposed architecture demonstrates how AI-driven automation can offer organisations a scalable, cost-effective, and practical alternative to traditional SOCs, which are often resource-intensive and require specialized personnel, strengthening resilience against contemporary cybersecurity threats and enabling multi-vendor support through adaptable data sources.eng
dc.identifier.tid204057582
dc.identifier.urihttp://hdl.handle.net/10400.8/14727
dc.language.isopor
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectCybersecurity
dc.subjectAI-driven security
dc.subjectNetwork automation
dc.subjectLarge language models
dc.subjectIntrusion detection
dc.subjectSoftware-defined networking
dc.titleNext‐Generation Network Management: Harnessing AI to Automate Operations
dc.typemaster thesis
dspace.entity.typePublication
thesis.degree.nameMestrado em Cibersegurança e Informática Forense

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