arXiv (All)
open access
RHINO: Guided Reasoning for Mapping Network Logs to Adversarial Tactics and Techniques with Large Language Models
Modern Network Intrusion Detection Systems (NIDS) generate vast volumes of low-level alerts that remain semantically fragmented, requiring labor-intensive manual correlation with high-level adversarial behaviors. Existing automation, namely rule-based systems and machine learning classifiers, has cr
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
- A Cross-Dataset based Zero-Day Intrusion Detection System by Integrating Siamese Network and Reinforcement Learning#1062018
- Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects#1108078
- Machine Learning-Based Intrusion Detection Framework for Detecting Security Attacks in Internet of Things#91748
- PairAudit: Guiding Human Review with Graph Tokens under Distribution Shift#1234627
- PairAudit: Guiding Human Review with Graph Tokens under Distribution Shift#1227244
Record · ID 1468128
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