Upload transfer learning anomaly detector model

README.md

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+---
+tags:
+  - anomaly-detection
+  - network-security
+  - transfer-learning
+  - isolation-forest
+  - autoencoder
+  - cybersecurity
+  - zero-day
+language:
+  - en
+license: apache-2.0
+datasets:
+  - nsl-kdd
+---
+
+# Network Anomaly Detection - Transfer Learning Model
+
+A transfer learning-based anomaly detection system designed to identify zero-day style attacks in network traffic.
+
+## Model Description
+
+This model combines:
+- **Variational Autoencoder (VAE)**: Pre-trained on large corpus of normal network traffic, fine-tuned on target domain
+- **Isolation Forest**: Trained on latent space representations for anomaly scoring
+
+### Architecture
+- Input Features: 7 network traffic features (protocol, packet length, inter-arrival time, ports, TCP flags)
+- Latent Dimension: 8
+- Encoder: 7 → 16 → 8
+- Decoder: 8 → 16 → 7
+
+## Training Details
+
+### Dataset
+- **Source**: NSL-KDD Intrusion Detection Dataset
+- **Pre-training**: 6,400 normal network samples
+- **Fine-tuning**: 8,000 mixed samples (80% normal, 20% attacks)
+- **Test Set**: 2,000 samples (80% normal, 20% attacks)
+
+### Training Configuration
+- Pre-training epochs: 50
+- Fine-tuning epochs: 10
+- Optimizer: Adam (learning rate: 0.001)
+- Loss: Reconstruction error (MSE)
+- Contamination parameter: 0.05 (5% expected anomaly rate)
+
+## Performance
+
+### Metrics
+| Metric | Value |
+|--------|-------|
+| Accuracy | 82.95% |
+| Precision | 74.38% |
+| Recall | 22.50% |
+| F1-Score | 34.55% |
+| ROC-AUC | 0.96 |
+
+### Interpretation
+- **High Precision (74.38%)**: Low false alarm rate - detected anomalies are highly likely to be real
+- **Excellent ROC-AUC (0.96)**: Model excellently ranks normal vs anomalous samples
+- **Optimized for Zero-Day Detection**: Focuses on high-confidence anomalies rather than catching all attacks
+
+## Feature Specifications
+
+### Input Features (7 total)
+1. **Protocol**: Encoded as TCP=1, UDP=2, ICMP=3, IGMP=4, GRE=5
+2. **Packet Length**: Bytes of packet payload
+3. **Packet Length Variance**: Standard deviation across packet sizes
+4. **Inter-arrival Time**: Time between consecutive packets (seconds)
+5. **High Port Indicator**: Binary flag for high-numbered ports
+6. **Low Port Indicator**: Binary flag for low-numbered ports
+7. **TCP Flags**: Present/absent indicator
+
+All features are standardized via StandardScaler before model input.
+
+## Usage
+
+### Python API
+```python
+import joblib
+import numpy as np
+from tensorflow import keras
+
+# Load model components
+detector = joblib.load("detector.pkl")
+autoencoder = keras.models.load_model("autoencoder/model.h5")
+
+# Prepare your data (shape: [n_samples, 7])
+X_new = np.array([[...]])  # Your traffic features
+
+# Get predictions
+predictions, latent_scores, recon_errors = detector.predict(X_new)
+# predictions: -1 = anomaly, 1 = normal
+# latent_scores: anomaly scores in latent space
+# recon_errors: reconstruction errors from autoencoder
+```
+
+### FastAPI Server
+```bash
+python -m src.api.server
+```
+
+Then POST to `/predict`:
+```json
+{
+  "src_ip": "192.168.1.100",
+  "dst_ip": "10.0.0.50",
+  "src_port": 54321,
+  "dst_port": 443,
+  "protocol": "TCP",
+  "packet_length": 512,
+  "inter_arrival": 0.001,
+  "flags": "SYN"
+}
+```
+
+## Model Limitations
+
+1. **Trained on specific dataset**: Best performance on NSL-KDD or similar network traffic patterns
+2. **Contamination parameter**: Assumes ~5% of traffic is anomalous; adjust for different environments
+3. **Feature dependencies**: Requires exact 7 features in standardized form
+4. **Recall trade-off**: Conservative detection (22.5% recall) to minimize false alarms
+
+## Fine-tuning for Your Domain
+
+To adapt this model to your network:
+
+```python
+from src.models.trainer import ModelTrainer
+
+trainer = ModelTrainer()
+
+# Your domain-specific traffic data
+X_target = load_your_traffic_data()  # shape: [n, 7]
+
+# Fine-tune the autoencoder
+history = trainer.finetune_with_strategy(
+    'freeze_encoder',  # or 'progressive', 'layer_wise'
+    X_target=X_target,
+    epochs=10,
+    learning_rate=0.0001
+)
+
+# Retrain Isolation Forest on new latent space
+trainer.train_transfer_learning(
+    X_pretrain=X_target,  # Use your data
+    X_finetune=X_target,
+    ae_epochs=0,  # Already fine-tuned
+    finetune_epochs=10
+)
+```
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@software{{anomaly_detector_2025,
+  title={{Network Anomaly Detection - Transfer Learning Model}},
+  author={{CyberSecurityTL Contributors}},
+  year={{2025}},
+  url={{https://huggingface.co/{repo_name}}}
+}}
+```
+
+## License
+
+Apache License 2.0 - See LICENSE file for details
+
+## Related Work
+
+- NSL-KDD Dataset: https://www.unb.ca/cic/datasets/nsl-kdd.html
+- Isolation Forest: Liu et al., 2008 (https://doi.org/10.1145/1541880.1541882)
+- Autoencoder Anomaly Detection: Sakurada & Yairi, 2014
+
+## Disclaimer
+
+This model is trained on network traffic patterns from 2009 (NSL-KDD dataset). It may not detect modern attack techniques. Always use in conjunction with other security tools and manual analysis.
+
+---
+
+Model created: 2025-12-21 10:22:44

autoencoder/autoencoder.h5

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autoencoder/config.json

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+{"input_dim": 41, "encoder_dims": [16, 8], "latent_dim": 16, "learning_rate": 0.001}

autoencoder/decoder.h5

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autoencoder/encoder.h5

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config.json

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+{
+  "model_type": "transfer_learning_anomaly_detector",
+  "architecture": {
+    "autoencoder": {
+      "input_dim": 7,
+      "latent_dim": 8,
+      "encoder_dims": [
+        16,
+        8
+      ],
+      "decoder_dims": [
+        8,
+        16
+      ],
+      "activation": "relu",
+      "output_activation": "sigmoid"
+    },
+    "isolation_forest": {
+      "n_estimators": 100,
+      "contamination": 0.05,
+      "random_state": 42
+    }
+  },
+  "training": {
+    "dataset": "NSL-KDD",
+    "pretrain_samples": 6400,
+    "finetune_samples": 8000,
+    "test_samples": 2000,
+    "pretrain_epochs": 50,
+    "finetune_epochs": 10
+  },
+  "performance": {
+    "accuracy": 0.8295,
+    "precision": 0.7438,
+    "recall": 0.225,
+    "f1_score": 0.3455,
+    "roc_auc": 0.96
+  },
+  "features": [
+    "protocol_encoded",
+    "packet_length",
+    "packet_length_variance",
+    "inter_arrival_time",
+    "high_port_indicator",
+    "low_port_indicator",
+    "tcp_flags"
+  ],
+  "feature_scaling": "StandardScaler (fitted on training data)",
+  "created_date": "2025-12-21T10:22:44.552573"
+}

detector.pkl

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+oid sha256:dfcec844c3f7f72a4a12453517426f98be539ecc751bb5270986e61a9a3965a5
+size 1051676

requirements.txt

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+scikit-learn>=1.0.0
+tensorflow>=2.10.0
+numpy>=1.21.0
+pandas>=1.3.0
+fastapi>=0.95.0
+pydantic>=1.8.0