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README.md

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+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.
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+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.
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+- 📊 Explore session data trends
+- 🔍 Predict intrusions in real time
+- 🤖 Model: LightGBM with threshold = 0.2
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+[🔗 Model Notebook](https://github.com/butlerem/intrusion-detection-model-lgbm)  
+[🔗 Dataset Source](https://www.kaggle.com/code/nukimayasari/cybersecurity-intrusion)
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