Kopie von Keyven/SecIDS-v2

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README.md

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+---
+language: en
+license: cc-by-nc-4.0
+tags:
+- automotive
+- intrusion-detection
+- can-bus
+- cybersecurity
+- temporal-cnn
+- pytorch-lightning
+- onnx
+- tensorrt
+datasets:
+- car-hacking-challenge-2021
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+library_name: pytorch
+---
+
+# SecIDS-v2: Next-Generation Automotive Intrusion Detection System
+
+![GitHub Banner](visuals/github_banner.png)
+
+## Model Description
+
+**SecIDS-v2** is a production-ready deep learning system for detecting cyber attacks on automotive CAN (Controller Area Network) buses. Built with Temporal Convolutional Networks (TCN), it achieves state-of-the-art performance while maintaining real-time inference speeds suitable for embedded deployment on NVIDIA Jetson devices.
+
+### Key Features
+
+- **High Performance**: 98.2% detection accuracy with 4.2ms inference latency on Jetson Nano
+- **Multi-Task Learning**: Simultaneous detection of multiple attack types (DoS, Fuzzy, Spoofing, Replay)
+- **Production-Ready**: Complete deployment pipeline with ONNX/TensorRT export, FastAPI server, and Streamlit dashboard
+- **Advanced Feature Engineering**: 25 CAN-specific features including temporal, payload, and statistical attributes
+- **Edge-Optimized**: INT8 quantization support for resource-constrained automotive ECUs
+
+## Architecture
+
+![Model Architecture](visuals/architecture.png)
+
+**SecIDS-v2** uses a Temporal Convolutional Network (TCN) with the following structure:
+
+- **Input**: Sliding windows of 128 CAN frames × 25 features
+- **TCN Backbone**: 3 blocks with dilated convolutions (32→64→128 filters, dilations 1→2→4)
+- **Receptive Field**: 128 frames (captures long-range temporal dependencies)
+- **Multi-Task Heads**: 4 classification heads for different attack types
+- **Parameters**: 3.8M (27% smaller than LSTM v1)
+- **Output**: Binary classification + attack type prediction
+
+## Performance
+
+![Performance Comparison](visuals/performance_comparison.png)
+
+### SecIDS v1 → v2 Improvements
+
+| Metric | LSTM v1 | TCN v2 | Improvement |
+|--------|---------|--------|-------------|
+| **Accuracy** | 97.2% | 98.2% | +1.0% |
+| **Inference (Jetson Nano)** | 18.5ms | 4.2ms | **4.4× faster** |
+| **Model Size** | 5.2M params | 3.8M params | -27% |
+| **F1-Score (DoS)** | 96.5% | 98.1% | +1.6% |
+| **F1-Score (Fuzzy)** | 95.8% | 97.9% | +2.1% |
+| **F1-Score (Spoofing)** | 96.2% | 98.5% | +2.3% |
+| **F1-Score (Replay)** | 97.1% | 98.3% | +1.2% |
+
+### Hardware Performance
+
+| Device | Precision | Latency | Throughput |
+|--------|-----------|---------|------------|
+| NVIDIA Jetson Nano | FP16 | 4.2ms | 238 FPS |
+| NVIDIA Jetson Nano | INT8 | 2.8ms | 357 FPS |
+| NVIDIA Jetson Xavier NX | FP16 | 1.9ms | 526 FPS |
+| Intel Core i7 (CPU) | FP32 | 12.5ms | 80 FPS |
+| NVIDIA RTX 4060 | FP32 | 0.8ms | 1250 FPS |
+
+## Feature Importance
+
+![Feature Importance](visuals/feature_importance.png)
+
+**Top 10 Most Important Features:**
+
+1. **Inter-Arrival Time (Δt)** - Time between consecutive frames
+2. **Payload Entropy** - Randomness of data payload
+3. **Hamming Distance** - Bit-level changes between frames
+4. **ID Change Frequency** - Rate of CAN ID transitions
+5. **DLC Variance** - Data Length Code variability
+6. **ID Occurrence Rate** - Frequency of specific CAN IDs
+7. **Payload Mean** - Average payload byte values
+8. **Payload Std Dev** - Payload variability
+9. **Time-Since-Last** - Time since last occurrence of ID
+10. **ID Diversity** - Number of unique IDs in window
+
+## Training Data
+
+**Primary Dataset**: [Car Hacking Challenge 2021](https://ocslab.hksecurity.net/Datasets/CAN-intrusion-dataset)
+
+- **Total Frames**: ~200,000 CAN frames
+- **Normal Traffic**: ~180,000 frames (90%)
+- **Attack Types**: DoS, Fuzzy, Spoofing, Gear Replay
+- **Attack Frames**: ~20,000 frames (10%)
+- **Train/Val Split**: 70/30
+- **Window Size**: 128 frames with 50% overlap
+
+### Data Preprocessing
+
+1. **Feature Extraction**: 25 engineered features per frame
+   - Temporal: Inter-arrival time, time-since-last, sequence position
+   - Payload: Entropy, mean, std, Hamming distance
+   - Statistical: Per-ID aggregates, DLC variance, ID diversity
+
+2. **Normalization**: StandardScaler (μ=0, σ=1)
+
+3. **Augmentation** (training only):
+   - Bit-flip injection (5% probability)
+   - Temporal jitter (±2ms)
+   - Random masking (10% features)
+
+## Intended Use
+
+### Primary Use Cases
+
+- **Automotive Cybersecurity**: Real-time intrusion detection in connected vehicles
+- **CAN Bus Monitoring**: Network anomaly detection in industrial/automotive systems
+- **Security Research**: Baseline model for CAN-bus attack detection research
+- **Education**: Reference implementation for automotive security courses
+
+### Out-of-Scope Use
+
+- **Non-CAN Protocols**: Not designed for FlexRay, LIN, or Ethernet automotive networks
+- **Safety-Critical Control**: Should not replace functional safety mechanisms (ISO 26262)
+- **Guaranteed Protection**: No ML model provides 100% security; defense-in-depth required
+
+## Limitations
+
+- **Training Data Bias**: Trained primarily on synthesized attack scenarios
+- **Zero-Day Attacks**: May not detect novel attack patterns not seen during training
+- **Context Dependence**: Performance may vary across different vehicle platforms
+- **Latency vs Accuracy Trade-off**: Optimized for speed; may miss subtle attacks
+- **False Positives**: ~1.8% false alarm rate may require tuning for production
+
+## Usage
+
+### Quick Start (Python)
+
+```python
+import torch
+from secids.models import TemporalCNN
+from secids.data import CANPreprocessor
+
+# Load model
+model = TemporalCNN.load_from_checkpoint("final_model.ckpt")
+model.eval()
+
+# Preprocess CAN data
+preprocessor = CANPreprocessor()
+features = preprocessor.transform(can_frames)  # [128, 25]
+
+# Inference
+with torch.no_grad():
+    logits = model(features.unsqueeze(0))  # [1, 128, 25]
+    pred = torch.argmax(logits, dim=-1)
+
+print(f"Attack Detected: {pred.item() == 1}")
+```
+
+### ONNX Deployment
+
+```python
+import onnxruntime as ort
+
+# Load ONNX model
+session = ort.InferenceSession("secids_v2.onnx")
+
+# Run inference
+outputs = session.run(None, {"input": features.numpy()})
+prediction = outputs[0].argmax()
+```
+
+### FastAPI Server
+
+```bash
+# Start REST API server
+cd serving
+python app.py
+
+# Make prediction request
+curl -X POST http://localhost:8080/predict \
+  -H "Content-Type: application/json" \
+  -d @can_sample.json
+```
+
+### Streamlit Dashboard
+
+```bash
+# Start web dashboard
+cd serving
+streamlit run dashboard.py --server.port 5060
+```
+
+## Training
+
+### Requirements
+
+```bash
+pip install torch torchvision pytorch-lightning
+pip install pandas numpy pyarrow
+pip install scikit-learn wandb
+```
+
+### Training Script
+
+```bash
+python scripts/train.py \
+  --model tcn \
+  --data data/processed/train.parquet \
+  --batch_size 32 \
+  --epochs 50 \
+  --gpus 1 \
+  --precision 16
+```
+
+### Hyperparameters
+
+- **Optimizer**: AdamW (lr=1e-3, weight_decay=1e-4)
+- **Scheduler**: ReduceLROnPlateau (patience=5, factor=0.5)
+- **Loss**: CrossEntropyLoss with class weights [1.0, 10.0]
+- **Batch Size**: 32
+- **Window Size**: 128 frames
+- **Stride**: 64 frames (50% overlap)
+- **Early Stopping**: Patience=10 epochs
+
+## Model Export
+
+### ONNX Export
+
+```python
+from secids.models import TemporalCNN
+import torch
+
+model = TemporalCNN.load_from_checkpoint("model.ckpt")
+dummy_input = torch.randn(1, 128, 25)
+
+torch.onnx.export(
+    model,
+    dummy_input,
+    "secids_v2.onnx",
+    input_names=["input"],
+    output_names=["output"],
+    dynamic_axes={"input": {0: "batch"}}
+)
+```
+
+### TensorRT Optimization
+
+```bash
+# Convert ONNX to TensorRT (FP16)
+trtexec --onnx=secids_v2.onnx \
+        --saveEngine=secids_v2_fp16.trt \
+        --fp16
+
+# Convert to INT8 (requires calibration data)
+trtexec --onnx=secids_v2.onnx \
+        --saveEngine=secids_v2_int8.trt \
+        --int8 \
+        --calib=calibration.cache
+```
+
+## Evaluation
+
+### Test Set Performance
+
+```bash
+python scripts/evaluate.py \
+  --model outputs/tcn_production/final_model.ckpt \
+  --data data/processed/test.parquet \
+  --output results/
+```
+
+**Outputs**:
+- Confusion matrix (PNG)
+- ROC/PR curves (PNG)
+- Per-attack-type metrics (JSON)
+- Latency profiling (CSV)
+
+### Benchmark Results
+
+| Dataset | Accuracy | Precision | Recall | F1-Score |
+|---------|----------|-----------|--------|----------|
+| Car Hacking (2021) | 98.2% | 97.8% | 98.6% | 98.2% |
+| HCRL (2020) | 97.5% | 96.9% | 98.1% | 97.5% |
+| SynCAN (2023) | 96.8% | 95.7% | 97.9% | 96.8% |
+
+## Citation
+
+```bibtex
+@software{secids_v2_2025,
+  author = {Hardani, Keyvan},
+  title = {SecIDS-v2: Next-Generation Automotive Intrusion Detection System},
+  year = {2025},
+  url = {https://github.com/Keyvanhardani/SecIDS-v2},
+  note = {Production-ready CAN-bus intrusion detection with Temporal CNNs}
+}
+```
+
+## Related Work
+
+- **SecIDS v1**: LSTM-based predecessor (97.2% accuracy, 18.5ms latency)
+- **CANnolo**: YOLO-inspired object detection approach
+- **GIDS**: Graph neural networks for CAN security
+- **Deep-CAN**: Autoencoder-based anomaly detection
+
+## Acknowledgments
+
+- **Dataset**: OCSLab HK Security (Car Hacking Challenge 2021)
+- **Framework**: PyTorch Lightning team
+- **Optimization**: NVIDIA TensorRT team
+- **Inspiration**: Temporal CNN architecture from Bai et al. (2018)
+
+## License
+
+MIT License - See [LICENSE](LICENSE) for details
+
+## Contact
+
+- **Author**: Keyvan Hardani
+- **GitHub**: [Keyvanhardani/SecIDS-v2](https://github.com/Keyvanhardani/SecIDS-v2)
+- **Issues**: [GitHub Issues](https://github.com/Keyvanhardani/SecIDS-v2/issues)
+- **Demo**: [secids.keyvan.ai](http://secids.keyvan.ai)
+- **Linkedin**  [Linkedin](https://www.linkedin.com/in/keyvanhardani/)
+
+---
+
+**Last Updated**: October 2025
+**Model Version**: 2.0.0
+**Framework**: PyTorch 2.0+, Lightning 2.0+

config.json

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+{
+  "model_type": "temporal_cnn",
+  "architecture": "SecIDS-v2",
+  "task": "intrusion-detection",
+  "framework": "pytorch",
+  "version": "2.1.0",
+
+  "model_config": {
+    "input_dim": 25,
+    "hidden_dim": 256,
+    "num_classes": 2,
+    "dropout": 0.1,
+    "num_channels": [256, 256, 512, 512],
+    "kernel_size": 3,
+    "num_layers": 4,
+    "activation": "relu"
+  },
+
+  "model_info": {
+    "num_parameters": 3815170,
+    "trainable_parameters": 3815170,
+    "model_size_mb": 15.2
+  },
+
+  "training_config": {
+    "dataset": "Car Hacking Challenge 2021",
+    "total_samples": 200000,
+    "train_split": 0.7,
+    "val_split": 0.15,
+    "test_split": 0.15,
+    "epochs": 100,
+    "batch_size": 64,
+    "learning_rate": 0.001,
+    "optimizer": "AdamW",
+    "window_size": 128
+  },
+
+  "performance": {
+    "accuracy": 0.982,
+    "f1_score": 0.975,
+    "precision": 0.983,
+    "recall": 0.978,
+    "latency_ms": {
+      "jetson_nano_int8": 4.2,
+      "jetson_xavier_fp16": 2.8,
+      "rtx_4060": 0.9,
+      "cpu_windows": 8.26
+    }
+  },
+
+  "attack_types": ["DoS", "Fuzzy", "Spoofing", "Replay"],
+
+  "pytorch_version": "2.0+",
+  "python_version": "3.8+",
+  "license": "cc-by-nc-4.0",
+  "author": "Keyvan Hardani",
+  "github": "https://github.com/Keyvanhardani/SecIDS-v2",
+  "dashboard": "https://secids.keyvan.ai"
+}

requirements.txt

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+torch>=2.0.0
+pytorch-lightning>=2.0.0
+pandas>=2.0.0
+numpy>=1.24.0
+scikit-learn>=1.3.0

secids_v2_tcn_model.ckpt

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