LLMs for Zero-Shot Threat Detection via Structured Risk Indicators
We propose a two-stage large language model (LLM) framework for zero-shot detection of insider threats and advanced persistent threats (APTs) from heterogeneous security logs. The framework models user activity as chronological timelines and incorporates retrieval-augmented generation (RAG) to provide personalised behavioural context from each user's historical activity. Rather than performing end-to-end classification directly from raw logs, it first generates structured, interpretable sets of threat-specific risk indicators, which are then classified jointly across temporal sequences to capture attack patterns spanning multiple windows.The framework is evaluated on two benchmark dataset...
| Authors | Abdullah Alghamdi, Siamak Layeghy, Marius Portmann |
|---|---|
| Primary category | cs.CR |
| Published | 17 August 2026 |
| Last updated at source | 17 August 2026 |
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