Monitoring an ML-Based Intrusion Detection System on AWS SageMaker
This article demonstrates how to deploy a Random Forest classifier as a real-time network intrusion detection system (IDS) on AWS SageMaker, and configure SageMaker Model Monitor to detect model degradation.
Why it matters
This approach provides a comprehensive monitoring framework for maintaining the performance of a mission-critical ML-based IDS system, helping to catch problems before they impact the production environment.
Key Points
- 1Deploy a Random Forest classifier trained on the UNSW-NB15 dataset as a real-time network IDS
- 2Configure SageMaker Model Monitor to detect model degradation and trigger CloudWatch alarms
- 3Set up a retraining and shadow-testing workflow to maintain model performance
Details
The article walks through the process of training a Random Forest model on the UNSW-NB15 dataset, which contains 42 features extracted from network packet headers and payloads. The trained model is then deployed as a real-time IDS on AWS SageMaker. To monitor the model's performance, the author configures SageMaker Model Monitor to capture every request/response pair and compare it to a statistical baseline. When the model starts to degrade, Model Monitor triggers CloudWatch alarms to notify the on-call engineer. The article also discusses setting up a retraining and shadow-testing workflow to ensure the model's continued effectiveness.
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