Sieve uses an LLM to generate executable queries from natural language security questions grounded by auto-extracted log-format context, cutting error rates over 3x on complex temporal and cross-event tasks versus manual scripting across 133 queries and 5 log types.
Using large language models for template detection from security event logs.International Journal of Information Security, 24
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
baseline 2
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
baseline 1polarities
baseline 1representative citing papers
Systematic review of 145 papers on LLM-based log analysis, providing a unified taxonomy, common design patterns, evaluation practices, and challenges for deployment under drift and limited labels.
citing papers explorer
-
Parser-Free Querying of Security Logs
Sieve uses an LLM to generate executable queries from natural language security questions grounded by auto-extracted log-format context, cutting error rates over 3x on complex temporal and cross-event tasks versus manual scripting across 133 queries and 5 log types.