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| title: Intrusion Detection Dashboard | |
| emoji: 🛡️ | |
| colorFrom: indigo | |
| colorTo: blue | |
| sdk: streamlit | |
| sdk_version: "1.31.1" | |
| app_file: app.py | |
| pinned: false | |
| This is a machine learning Streamlit app that predicts potential cyberattacks based on real-time session characteristics like IP reputation, login attempts, and encryption type. | |
| It uses a LightGBM classifier trained on a labeled intrusion detection dataset. The model prioritizes **recall** to minimize undetected attacks and is deployed via a Hugging Face API. | |
| - 📊 Explore session data trends | |
| - 🔍 Predict intrusions in real time | |
| - 🤖 Model: LightGBM with threshold = 0.2 | |
| [🔗 Model Notebook](https://github.com/butlerem/intrusion-detection-model-lgbm) | |
| [🔗 Dataset Source](https://www.kaggle.com/code/nukimayasari/cybersecurity-intrusion) | |