ESTG - Mestrado em Cibersegurança e Informática Forense
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Browsing ESTG - Mestrado em Cibersegurança e Informática Forense by advisor "Correia, Luís Filipe Jesus"
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- HaLert: A Resilient Smart City Architecture for Post-Disaster Based on Wi-Fi HaLow Mesh and SDNPublication . Ortigoso, Ana Rita Lameiro; Frazão, Luís Alexandre Lopes; Fuentes, Daniel Alexander Lopes; Correia, Luís Filipe Jesus; Costa, Nuno Alexandre Ribeiro da; Pereira , António Manuel de JesusCatastrophic events are often unpredictable, making the reuse of existing infrastructures crucial to developing alternative communication strategies that minimise their impact on public communication and the timely dissemination of official alerts. The advent of smart cities, characterised by dense and geographically distributed IoT networks, offers significant potential for such reuse. Furthermore, in post-disaster contexts, uncertainty and panic can endanger public safety and hinder rescue operations, particularly in a hyperconnected world where communication failures and misinformation can intensify these risks. This dissertation presents HaLert, a resilient smart city architecture based on a Wi- Fi HaLow IEEE 802.11s mesh network, enabling rapid resource reallocation to support an emergency communication system for exchanging text, location, image, audio, and video between citizens and authorities. The system also delivers verified, emotionaware information through Armando, a chatbot integrating Retrieval-Augmented Generation (RAG) with cumulative context tracking. Network monitoring and configuration are facilitated by Software-Defined Networking (SDN) via a LoRa controlledflooding mesh network. A prototype of HaLert architecture was implemented and tested in real urban indoor and outdoor scenarios. Results show that despite environmental impacts on latency (15–54.8 ms) and throughput (134–726 Kbps upload, 117–682 Kbps download), the Wi-Fi HaLow network remained stable, while the LoRa network achieved a 94.96% average message success rate.
- Next‐Generation Network Management: Harnessing AI to Automate OperationsPublication . Vieira, Gabriel Madeira; Fuentes, Daniel Alexandre Lopes; Frazão, Luís Alexandre Lopes; Correia, Luís Filipe Jesus; Costa, Nuno Alexandre Ribeiro da; Pereira, António Manuel de JesusCybersecurity 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.