LogLLaMA is a novel framework that fine-tunes LLaMA2 on normal log data to effectively detect anomalous log messages that may indicate security breaches or system failures.

• Uses transfer learning to adapt LLaMA2 to understand log message patterns
• Trained on three large-scale datasets to learn normal behavior patterns
• Identifies anomalies by detecting deviations from expected log sequences
• Demonstrates the power of LLMs for specialized security applications

This research advances cybersecurity capabilities by enabling more accurate detection of unusual system behavior, potentially identifying threats before they cause damage, and reducing false positives in security monitoring systems.

LogLLaMA: Transformer-based log anomaly detection with LLaMA