FlexLog introduces a novel hybrid approach that leverages both LLMs and traditional ML to detect anomalies in unstable log data with minimal training requirements.

• Addresses the real-world challenge of unstable logs that change due to software updates or environmental shifts
• Achieves superior detection accuracy while requiring significantly less training data
• Combines the strengths of decision trees, k-nearest neighbors, and LLMs in an innovative hybrid architecture
• Demonstrates practical security applications for identifying potential breaches and system vulnerabilities

This approach represents a meaningful advance for security operations teams who need to maintain effective anomaly detection despite constantly evolving log formats and structures.

Original Paper: LLM meets ML: Data-efficient Anomaly Detection on Unseen Unstable Logs