This research introduces an innovative approach that leverages Large Language Models to detect patterns in unstructured security event logs - a critical capability for identifying cyber threats.

• Enables automated template detection from security logs without manual rule creation
• Improves real-time attack detection through efficient pattern recognition
• Enhances security incident analysis by identifying hidden correlations in log data
• Provides scalable solutions for processing massive volumes of unstructured log data

For security teams, this research offers a practical way to transform overwhelming log data into actionable security insights, potentially reducing detection time and improving threat response capabilities.

Using Large Language Models for Template Detection from Security Event Logs