Federated Attack Campaign Detection via Contrastive Encoding of Threat Indicators in Gradient Updates

This paper presents a federated learning approach to detect coordinated cyber attack campaigns by analyzing gradient updates from distributed models without exposing raw threat data. The method uses contrastive encoding to identify patterns of malicious activity across organizations while preserving privacy—enabling collaborative defense without centralizing sensitive security information.

This paper presents a federated learning approach to detect coordinated cyber attack campaigns by analyzing gradient updates from distributed models without exposing raw threat data. The method uses contrastive encoding to identify patterns of malicious activity across organizations while preserving privacy—enabling collaborative defense without centralizing sensitive security information.

High-quality data pipelines remain the most consistent bottleneck in enterprise AI maturity. QuettaMinds helps clients close the gap between raw data assets and production-ready AI inputs.