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Next‐Generation Network Management: Harnessing AI to Automate Operations
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| 11.86 MB | Adobe PDF |
Autores
Resumo(s)
Cybersecurity 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.
Descrição
Palavras-chave
Cybersecurity AI-driven security Network automation Large language models Intrusion detection Software-defined networking