This research pioneers the application of Large Language Models to log anomaly detection while addressing computational cost constraints.

• Explores parameter-efficient fine-tuning (PEFT) techniques for LLMs in security monitoring
• Demonstrates how LLMs can identify atypical patterns in system logs without full fine-tuning
• Provides a cost-effective approach to enhance security monitoring capabilities
• Fills a significant research gap in applying LLMs to log anomaly detection

This advancement matters for security teams who need more efficient, accurate methods to detect potential system threats without prohibitive computational costs.

Adapting Large Language Models for Parameter-Efficient Log Anomaly Detection