| | --- |
| | 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") |
| | |
