arXiv (All)
open access
Security Logs to ATT&CK Insights: Leveraging LLMs for High-Level Threat Understanding and Cognitive Trait Inference
Understanding adversarial behavior in cybersecurity has traditionally relied on high-level intelligence reports and manual interpretation of attack chains. However, real-time defense requires the ability to infer attacker intent and cognitive strategy directly from low-level system telemetry such as
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- AI-Driven Cybersecurity and Threat Intelligence#671676
- AI-Driven Cybersecurity: Transforming the Prevention of Cyberattacks#84355
- AI-Driven Threat Detection and Risk Mitigation in Modern Cybersecurity Systems#695790
- MACHINE LEARNING IN CYBERSECURITY#1178739
- CTIFoundry: An Agent-Native Corpus Scaffold for Cyber Threat Intelligence#1199944
Record · ID 1488255
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive