This research showcases how Large Language Models can transform raw log data into structured formats with minimal human intervention, enhancing security monitoring capabilities.

• Leverages self-generated in-context learning where the model creates its own examples to better understand log structures
• Implements a self-correction mechanism that enables the model to identify and fix parsing errors
• Achieves superior performance compared to traditional rule-based and learning-based log parsers
• Demonstrates adaptability to evolving log formats without requiring retraining

For security teams, this approach means more accurate anomaly detection, fewer false positives, and better incident response through improved log analysis capabilities.

Log Parsing using LLMs with Self-Generated In-Context Learning and Self-Correction