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

2025/06/06 by Long Dang, Thushari Hapuarachchi, Dang, Long +5
Computer Science · Engineering · #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Smart Grid Security and Resilience #Software-Defined Networks and 5G #Vehicular Ad Hoc Networks (VANETs)

paper · pdf · doi:10.48550/arxiv.2506.06556

openalex publication_date 2025/06/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• ML Attack Models: Adversarial Attacks and Data Poisoning Attacks
• An Adversarial Attack Defending System for Securing In-Vehicle Networks
• A Survey on Security Attacks and Defense Techniques for Connected and Autonomous Vehicles
• Smooth Adversarial Training
• Long Short-Term Memory Neural Networks for False Information Attack Detection in Software-Defined In-Vehicle Network
• Adversarial Examples Are Not Bugs, They Are Features
• Theoretically Principled Trade-off between Robustness and Accuracy
• Adversarial Robustness Toolbox v1.0.0
• State-of-the-Art Survey on In-Vehicle Network Communication (CAN-Bus) Security and Vulnerabilities
• Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
• Cybersecurity Attacks in Vehicle-to-Infrastructure (V2I) Applications and their Prevention
• The 2015 Ukraine Blackout: Implications for False Data Injection Attacks
• Towards Deep Learning Models Resistant to Adversarial Attacks
• Adversarial examples in the physical world
• DeepFool: a simple and accurate method to fool deep neural networks
• Adam: A Method for Stochastic Optimization
• Explaining and Harnessing Adversarial Examples

Related

• Long Short-Term Memory Neural Networks for False Information Attack Detection in Software-Defined In-Vehicle Network
• An Adversarial Attack Defending System for Securing In-Vehicle Networks
• A Robust Multi-Stage Intrusion Detection System for In-Vehicle Network Security using Hierarchical Federated Learning
• Detection of Message Injection Attacks onto the CAN Bus using Similarity of Successive Messages-Sequence Graphs
• Anomaly Detection in Intra-Vehicle Networks