Create README.md

README.md

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+---
+license: apache-2.0
+language: en
+library_name: keras
+tags:
+- intrusion-detection
+- cyber-physical-systems
+- iot-security
+- lstm
+- time-series
+- cybersecurity
+datasets:
+- ToN_IoT
+---
+
+# ClimIDS: Sensor-Layer Intrusion Detection System
+
+This model card is for **ClimIDS**, a lightweight, LSTM-based intrusion detection system (IDS) for the physical sensor layer of IoT deployments.
+
+## Model Description
+ClimIDS analyzes time-series data from environmental sensors (temperature, pressure, humidity) to detect anomalies in climate-monitoring systems. Its lightweight architecture (~5,000 parameters) makes it suitable for edge devices.
+
+- **Architecture:** `LSTM -> Dropout -> Dense -> Dense (Sigmoid)`
+- **Dataset:** Trained on `IoT_Weather` subset of ToN_IoT
+- **Performance:** 98.81% accuracy, 99.7% attack recall
+
+## Intended Use
+- **Primary Use:** Real-time binary classification of sensor telemetry
+- **Input:** `(batch_size, 10, 3)` — features `[temperature, pressure, humidity]`, normalized
+- **Output:** Float between 0.0 (Normal) and 1.0 (Attack), threshold 0.5
+
+## How to Use
+```python
+import tensorflow as tf
+import numpy as np
+from huggingface_hub import hf_hub_download
+
+MODEL_PATH = hf_hub_download("Codelord01/sensor_binary", "sensor_binary.keras")
+model = tf.keras.models.load_model(MODEL_PATH)
+model.summary()
+
+sample_data = np.random.rand(1, 10, 3).astype(np.float32)
+prediction_prob = model.predict(sample_data)
+predicted_class = 1 if prediction_prob > 0.5 else 0
+print(f"Prediction Probability: {prediction_prob:.4f}")
+print("Anomaly Detected" if predicted_class == 1 else "Normal Conditions")