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Use of LLMs as a support layer for attack explanation, mitigation recommendation and human-in-the-loop incident analysis.
DEFENDIS develops a framework for uniquely identifying IoT devices in a distributed manner while solving security threats through decentralized federated learning.
View ProjectA European research project on methods and proofs of concept for supporting cyber defence incident management.
View ProjectROBUST-6G studies security mechanisms for 6G systems, including monitoring, secure data management, trustworthy AI services, federated learning, and threat response.
View ProjectA comprehensive cybersecurity framework for Brain-Computer Interface systems in advanced driver assistance scenarios, focusing on detecting and preventing cyberattacks on the BCI lifecycle.
View ProjectA practical note on how distributed monitoring, DFL models, alert evidence and LLM-based support can fit into a cyberdefense workflow.
A research note on using DFL to turn distributed telemetry, anomalies and trust signals into cyberdefense situational awareness.
A technical note on how drone fleets can use DFL to collaborate on detection models without exposing mission telemetry.
A focused research note on median, trimmed mean and trust-weighted aggregation for peer-to-peer federations under poisoning and unreliable clients.
A research note on edge training, secure aggregation and adaptive privacy budgets for IoT security monitoring.