#Anomaly Detection
Anomaly detection identifies unusual patterns that may indicate fraud, security breaches, or system failures. Articles cover Go-based anomaly detection for financial security, statistical and ML-based approaches, and the engineering patterns for real-time anomaly detection in production systems.
2 posts tagged with anomaly detection. ← All posts
When fraud labels are scarce or the attack is brand new, unsupervised anomaly detection buys you a signal before you ever have a training set.
When you have few or no labels: unsupervised anomaly detection.
Two signals do most of the work for detecting compromised sessions: impossible travel between consecutive logins, and credential-stuffing density across an IP range. The Go implementation.
All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.