Create README.md

README.md

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+---
+license: apache-2.0
+language: en
+library_name: keras
+tags:
+- intrusion-detection
+- network-security
+- iot-security
+- cnn
+- bilstm
+- time-series
+- cybersecurity
+datasets:
+- CICIoT2023
+---
+
+# Binary Network-Layer Cyber-Physical IDS
+
+A hybrid **CNN-BiLSTM** model for real-time binary network intrusion detection in IoT environments.  
+This model acts as the first line of defense by quickly distinguishing between malicious and legitimate traffic.
+
+## Model Description
+
+- **Architecture:** `Conv1D -> ... -> Bidirectional LSTM -> Dense -> Dense (Sigmoid)`  
+- **Dataset:** Balanced subset of CICIoT2023  
+- **Performance:** 99.9997% accuracy  
+- **Limitations:** Validated only on CICIoT2023-like network traffic; may not detect novel attack types. Input must be normalized.  
+- **Training Information:**  
+  - Optimizer: Adam  
+  - Loss: Binary Cross-Entropy  
+  - Balanced dataset: 2 million samples (1M benign, 1M attack)  
+
+## Intended Use
+- **Primary Use:** Real-time network intrusion detection  
+- **Input:** `(batch_size, 10, 46)` — 46 network flow features, normalized  
+- **Output:** Float between 0.0 (Benign) 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
+
+# Download the model from Hugging Face
+MODEL_PATH = hf_hub_download("Codelord01/binary_model", "binary_model.keras")
+model = tf.keras.models.load_model(MODEL_PATH)
+model.summary()
+
+# Prepare a sample input: 1 sample, 10 timesteps, 46 features
+sample_data = np.random.rand(1, 10, 46).astype(np.float32)
+
+# Make a prediction
+prediction_prob = model.predict(sample_data)
+predicted_class = 1 if prediction_prob > 0.5 else 0
+
+print(f"Prediction Probability: {prediction_prob:.4f}")
+print("Malicious Traffic Detected" if predicted_class == 1 else "Benign Traffic")
+
+
+@mastersthesis{ababio2025multilayered,
+  title={A Multi-Layered Hybrid Deep Learning Framework for Cyber-Physical Intrusion Detection in Climate-Monitoring IoT Systems},
+  author={Awuni David Ababio},
+  year={2025},
+  school={Kwame Nkrumah University of Science and Technology}
+}