Codelord01/sensor_binary

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

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")