RHINO: Guided Reasoning for Mapping Network Logs to Adversarial Tactics and Techniques with Large Language Models
RHINO applies guided LLM reasoning to automatically map fragmented NIDS alerts to MITRE ATT&CK tactics, addressing the alert-triage bottleneck that plagues resource-constrained SOC teams.
Summary written by editorial AI · Source link below
arXiv:2510.14233v2 Announce Type: replace Abstract: 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 critical limitations: rule-based approaches fail to adapt to novel attack variations, while machine learning methods lack contextual awareness and tr
Editorial Analysis
SOC teams drowning in low-level alerts need semantic enrichment; LLM-guided ATT&CK mapping could accelerate triage without expanding headcount.
Evaluate LLM-based alert-to-ATT&CK mapping tools in your SOC lab to determine whether they materially reduce mean-time-to-triage.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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