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

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

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

Top 10 Most Important Features:

Inter-Arrival Time (Δt) - Time between consecutive frames
Payload Entropy - Randomness of data payload
Hamming Distance - Bit-level changes between frames
ID Change Frequency - Rate of CAN ID transitions
DLC Variance - Data Length Code variability
ID Occurrence Rate - Frequency of specific CAN IDs
Payload Mean - Average payload byte values
Payload Std Dev - Payload variability
Time-Since-Last - Time since last occurrence of ID
ID Diversity - Number of unique IDs in window

Training Data

Primary Dataset: Car Hacking Challenge 2021

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

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
Normalization: StandardScaler (μ=0, σ=1)
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 for details

Contact

Author: Keyvan Hardani
GitHub: Keyvanhardani/SecIDS-v2
Issues: GitHub Issues
Demo: secids.keyvan.ai
Linkedin Linkedin

Last Updated: October 2025 Model Version: 2.0.0 Framework: PyTorch 2.0+, Lightning 2.0+

keyvan-ai/SecIDS-v2

SecIDS-v2: Next-Generation Automotive Intrusion Detection System

Model Description

Key Features

Architecture

Performance

SecIDS v1 → v2 Improvements

Hardware Performance

Feature Importance

Training Data

Data Preprocessing

Intended Use

Primary Use Cases

Out-of-Scope Use

Limitations

Usage

Quick Start (Python)

ONNX Deployment

FastAPI Server

Streamlit Dashboard

Training

Requirements

Training Script

Hyperparameters

Model Export

ONNX Export

TensorRT Optimization

Evaluation

Test Set Performance

Benchmark Results

Citation

Related Work

Acknowledgments

License

Contact

keyvan-ai/
SecIDS-v2