Unlocking Log Mysteries: How Collaborative AI Log Anomaly Detection Achieves 99.99% Accuracy

When Your System Logs Speak: How CoLog’s Collaborative AI Listens for Both Whispers and Shouts Direct Answer: CoLog is a unified deep learning framework that detects both individual log anomalies and collective anomaly patterns by treating logs as a multimodal sentiment analysis problem. It achieves near-perfect accuracy (99.99% average F1-score) by using collaborative transformers that enable semantic and sequential log modalities to teach each other, rather than working in isolation. What Makes Log Anomaly Detection So Challenging? Central Question: Why do traditional log analysis methods fail to catch sophisticated attacks and system failures? Operating systems generate logs like a running …