How do you implement offline AI for anomaly detection in network security?

Offline network anomaly detection deploys ML models on network appliances or security servers within the organizational perimeter. The system captures network traffic metadata including source and destination addresses, ports, protocols, packet sizes, timing patterns, and connection durations. Feature engineering creates statistical profiles of normal traffic patterns per host, subnet, and time period. Unsupervised models like autoencoders, isolation forests, and one-class SVMs learn normal behavior and flag deviations. Deep learning models like LSTM autoencoders capture temporal patterns in traffic sequences. The system detects port scanning, data exfiltration, command and control communication, lateral movement, and unusual data transfer patterns. All analysis occurs on-premises, which is critical since the network traffic data itself is sensitive. Alerts integrate with the local SIEM for investigation and response.