RAGLog: Log Anomaly Detection using Retrieval Augmented Generation

Understand

The ability to detect log anomalies from system logs is a vital activity needed to ensure cyber resiliency of systems.

• It is applied for fault identification or facilitate cyber investigation and digital forensics.
• However, as logs belonging to different systems and components differ significantly, the challenge to perform such analysis is humanly challenging from the volume, variety and velocity of logs.
• This is further complicated by the lack or unavailability of anomalous log entries to develop trained machine learning or artificial intelligence models for such purposes.