Network Anomaly Detection with Ensemble ML

• Process and clean large network traffic logs
• Implement baseline classification models
• Design an ensemble architecture for high accuracy
• Integrate SHAP or LIME for model explainability

Follow these steps in order. Each one builds on the previous.

1. Step 1

   Set up the Python environment and install required machine learning libraries

2. Step 2

   Load and preprocess the network traffic dataset using pandas

3. Step 3

   Train baseline models including Random Forest and Support Vector Machines

4. Step 4

   Develop an ensemble model combining the baseline predictions

5. Step 5

   Evaluate the ensemble model using accuracy and F1 score metrics

6. Step 6

   Integrate Explainable AI using SHAP to interpret model decisions

7. Step 7

   Document the findings and model performance in a final report

• scikit-learn ensemble guide
• SHAP documentation