# SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks

arXiv: https://arxiv.org/abs/2506.06556 · HTML: https://mlsanity.com/paper/2506.06556/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 45 references that are in the map.
- First posted on arXiv 6 Jun 2025.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Theoretically Principled Trade-off between Robustness and Accuracy](https://mlsanity.com/paper/1901.08573/) (2019)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples](https://mlsanity.com/paper/1802.00420/) (2018)
- [Adversarial Examples Are Not Bugs, They Are Features](https://mlsanity.com/paper/1905.02175/) (2019)
- [Adversarial Robustness Toolbox v1.0.0](https://mlsanity.com/paper/1807.01069/) (2018)
- [Smooth Adversarial Training](https://mlsanity.com/paper/2006.14536/) (2020)
- [ML Attack Models: Adversarial Attacks and Data Poisoning Attacks](https://mlsanity.com/paper/2112.02797/) (2021)
- [A Survey on Security Attacks and Defense Techniques for Connected and Autonomous Vehicles](https://mlsanity.com/paper/2007.08041/) (2020)
- [An Adversarial Attack Defending System for Securing In-Vehicle Networks](https://mlsanity.com/paper/2008.11278/) (2020)
- [State-of-the-Art Survey on In-Vehicle Network Communication (CAN-Bus) Security and Vulnerabilities](https://mlsanity.com/paper/1802.01725/) (2018)
- [Long Short-Term Memory Neural Networks for False Information Attack Detection in Software-Defined In-Vehicle Network](https://mlsanity.com/paper/1906.10203/) (2019)
- [Cybersecurity Attacks in Vehicle-to-Infrastructure (V2I) Applications and their Prevention](https://mlsanity.com/paper/1711.10651/) (2017)

## Abstract (arXiv)

As the development of autonomous and connected vehicles advances, the complexity of modern vehicles increases, with numerous Electronic Control Units (ECUs) integrated into the system. In an in-vehicle network, these ECUs communicate with one another using an standard protocol called Controller Area Network (CAN). Securing communication among ECUs plays a vital role in maintaining the safety and security of the vehicle. This paper proposes a robust SDN-based False Data Detection and Mitigation System (FDDMS) for in-vehicle networks. Leveraging the unique capabilities of Software-Defined Networking (SDN), FDDMS is designed to monitor and detect false data injection attacks in real-time. Specifically, we focus on brake-related ECUs within an SDN-enabled in-vehicle network. First, we decode raw CAN data to create an attack model that illustrates how false data can be injected into the system. Then, FDDMS, incorporating a Long Short Term Memory (LSTM)-based detection model, is used to identify false data injection attacks. We further propose an effective variant of DeepFool attack to evaluate the model's robustness. To countermeasure the impacts of four adversarial attacks including Fast gradient descent method, Basic iterative method, DeepFool, and the DeepFool variant, we further enhance a re-training technique method with a threshold based selection strategy. Finally, a mitigation scheme is implemented to redirect attack traffic by dynamically updating flow rules through SDN. Our experimental results show that the proposed FDDMS is robust against adversarial attacks and effectively detects and mitigates false data injection attacks in real-time.