Unified Real-Time Anomaly Detection Across Retail Fraud and Network Intrusion Streams Using Dependency-Aware Feature Extraction

Why It Matters

Accurate, low‑latency detection across heterogeneous data streams reduces fraud loss and cyber‑risk while meeting strict performance SLAs, a critical advantage for retailers and security teams.

Key Takeaways

• Unified schema bridges retail and network data
• Temporal and contextual features raise AUPRC to 0.90
• LightGBM outperforms LSTM and other baselines
• Inference sustains 55k‑62k events/s, latency <0.026 ms

Pulse Analysis

Real‑time anomaly detection has long been hampered by the divergent nature of retail fraud logs and network intrusion telemetry. Both domains suffer extreme class imbalance, where fraudulent or malicious events constitute a fraction of total traffic, and they demand sub‑millisecond response times to prevent damage. Traditional pipelines treat each stream in isolation, missing cross‑domain patterns that could improve early warning capabilities. By normalizing disparate inputs into a shared event schema and applying domain masking, the new system creates a unified view that preserves essential context while avoiding feature leakage.

The core innovation lies in dependency‑aware feature extraction. Temporal attributes are computed strictly from past data per entity, such as time since the last transaction or capped counts of recent activity, ensuring compliance with real‑time constraints. A train‑derived entity‑frequency metric captures typical behavior, then safely transferred to validation and test sets. When fed into LightGBM, these enriched features drive AUROC above 0.95 and AUPRC beyond 0.90, eclipsing baseline models including logistic regression, random forests, isolation forests, and an LSTM sequence model. The tree‑based ensemble’s ability to handle heterogeneous features efficiently makes it especially suited for the unified pipeline.

Operational testing confirms the solution’s scalability. Micro‑batching delivers throughput of 55,000 to 62,000 events per second while keeping the 99th‑percentile latency under 0.026 ms per event, comfortably within most enterprise SLAs. This performance opens the door for organizations to consolidate fraud and security monitoring, reducing infrastructure complexity and fostering faster incident response. As data volumes grow and attacks become more sophisticated, such unified, low‑latency detection frameworks will become a cornerstone of resilient digital commerce and network defense strategies.