metadata
license: apache-2.0
language:
- en
metrics:
- accuracy
pipeline_tag: tabular-classification
library_name: sklearn
tags:
- stacking-ensemble
- xgboost
- catboost
- lightgbm
- adaboost
- randomforest
model-index:
- name: stacking-ensemble-learning
results:
- task:
type: tabular-classification
name: Network Intrusion Detection
dataset:
name: CSE-CIC-IDS2018 Cleaned
type: cicids2018
url: >-
https://www.kaggle.com/datasets/ekkykharismadhany/csecicids2018-cleaned
metrics:
- type: accuracy
value: 1
name: Accuracy (Meta Model)
- type: f1
value: 1
name: F1 Score (Meta Model)
- type: precision
value: 1
name: Precision (Meta Model)
- type: recall
value: 1
name: Recall (Meta Model)
IDS Stacking Ensemble Learning
Stacking ensemble (XGBoost + CatBoost + LightGBM + AdaBoost) for base mdoel with Random Forest as meta-model for Network Intrusion Detection System (IDS).
Metrics (test set)
| Model | Accuracy | F1 |
|---|---|---|
| XGBoost | 1.0000 | 1.0000 |
| CatBoost | 0.9998 | 0.9998 |
| LightGBM | 0.4441 | 0.5403 |
| AdaBoost | 0.9911 | 0.9907 |
Requirements
pip install scikit-learn xgboost catboost lightgbm pandas numpy joblib huggingface_hub
Usage
import joblib
import numpy as np
from huggingface_hub import hf_hub_download
models = {k: joblib.load(hf_hub_download("mrsindhunugroho/stacking-ensemble-learning", f"models/{k}_model.pkl"))
for k in ["xgboost", "catboost", "lightgbm", "adaboost"]}
meta = joblib.load(hf_hub_download("mrsindhunugroho/stacking-ensemble-learning", "models/meta_model.pkl"))
le = joblib.load(hf_hub_download("mrsindhunugroho/stacking-ensemble-learning", "models/label_encoder.pkl"))
base_preds = np.column_stack([m.predict(X) for m in models.values()])
y_pred = le.inverse_transform(meta.predict(base_preds))
Dataset
- Source: Kaggle — CSE-CIC-IDS2018 Cleaned
- Original: Canadian Institute for Cybersecurity (CSE-CIC-IDS2018)
- Preprocessing: sampling, label encoding, imputation, feature sanitization
Author
Sindhu Nugroho — ORCID