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AI Research
arXiv CS.AI·Jun 10·5 min read
A Unified Siamese Learning Framework for Zero-Day Anomaly Detection and Classification in Optical Networks
TL;DR
Zero-day anomalies in optical networks pose significant detection challenges due to their unexpected nature. A multi-similarity Siamese neural network was developed to unify zero-day anomaly detection and one-shot classification.
✦ Why It Matters
Engineers can implement this framework to enhance anomaly detection in optical networks without extensive retraining.
Key Takeaways
- Adopt a multi-similarity Siamese neural network for anomaly detection.
- Utilize one-shot learning to classify new anomalies instantly.
- Achieve over 99% accuracy in detection without retraining.
- Enhance network reliability by quickly adapting to unseen anomalies.
How It Works
The framework employs a multi-similarity Siamese neural network, which compares input data pairs to identify anomalies. By using a one-shot learning approach, it can classify new anomaly types based on a single example, allowing for rapid adaptation without retraining.