Results show that local inference over graph-based behavioral descriptions can make automated ATT&CK mapping from kernel-level telemetry operationally viable, without compromising data confidentiality.
Matteo Lupinacci, Luigi Arena, Francesco Blefari et al.· 0 citations
BEACON is an LLM-driven framework for cross-source CTI knowledge graph construction that constructs and releases two human-annotated datasets from 34 sources and outperforms all baselines by at least 23% and 9%, respectively.
Together, relational graph modeling, long-memory forecasting, explainability, and ATT&CK-aligned context turn passive honeypot infrastructure into a proactive early warning capability.
R. Sehgal, Rakesh Matam· Frontiers of Computer Scienc...· 0 citations
Large Language Models (LLMs) are increasingly used in cybersecurity, where accurate analysis often requires multi-step and context-dependent reasoning over complex and heterogeneous data. However, existing prompting approaches typically focus on eliciting reasoning without explicitly considering how intermediate reason...
Ji-Ling Zhou, Aisvarya Adeseye, Antti Hakkala et al.· 0 citations
EASE-CloudNet is a two-phase safety-alignment framework for generative small language models (SLMs) deployed on resource-constrained edge nodes that formulate deployment cost as a differentiable gate-conditioned expectation, so measured latency and energy constants affect the router through its reasoning probability.
EVCS-SecOnt enhances the interpretability, reusability, and explainability of EVCS cybersecurity management by bridging operational data with semantic intelligence, providing a semantic foundation for next-generation e-mobility and smart charging infrastructures.
Talea Huraysi, Ohud Alsadi, T. Pamulapati et al.· Electronics· 0 citations
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