Impact Highlights
Reduced false positives with adaptive thresholding in streaming pipelines.
Modeled contextual event dependencies beyond frequency-only methods.
Extended architecture toward dynamic graph modeling with TGNs.
Mission Brief
Real-time Log Anomaly Detection
A hybrid anomaly detection system for high-velocity logs that combines statistical baselines with transformer sequence modeling.
PyTorchDrain3scikit-learnIsolation ForestTransformers
Build Notes
Built a hybrid framework that fuses Isolation Forest with transformer sequence learning for contextual anomaly detection.
Implemented a self-supervised next-template prediction transformer to learn sequential dependencies in server logs.
Engineered an online feedback loop with adaptive threshold calibration to reduce false positives in real-time streams.
Extended the architecture to Temporal Graph Networks to improve inter-service dependency tracking.
Image Direction
Recommended Concept
A cyber operations wall with flowing log streams, heat signatures, and anomaly spikes emerging from a structured grid.
Text-to-Image Prompt
Futuristic SOC dashboard, midnight black background, emerald and cyan telemetry lines, anomaly nodes glowing in amber, cinematic volumetric lighting, high-detail UI overlays, wide composition, no text
Fallback asset in use: /illustrations/log-anomaly.svg