Vulnerabilities of Federated Learning in Autonomous Driving: Novel Poisoning Attacks and Mitigation Strategies

Khái niệm cốt lõi

Federated learning is susceptible to poisoning attacks that can deteriorate the global model performance or alter its behavior. This paper introduces two novel attacks, FLStealth and Off-Track Attack, tailored for regression tasks in autonomous driving, and evaluates their effectiveness against common defense mechanisms.

Tóm tắt

The paper investigates the vulnerabilities of federated learning (FL) in the context of autonomous driving, where regression tasks such as vehicle trajectory prediction are common. It introduces two novel poisoning attacks:

1. FLStealth: An untargeted attack that aims to deteriorate the global model performance while appearing benign.
2. Off-Track Attack (OTA): A targeted backdoor attack that alters the global model's behavior when exposed to a specific trigger.

The authors conduct comprehensive experiments using the Zenseact Open Dataset (ZOD) to assess the impact of these attacks and the effectiveness of various defense mechanisms, including robust aggregation techniques and anomaly detection.

The key findings are:

• FLStealth is highly effective in bypassing most defense strategies, except for Loss Defense and LossF usion.
• OTA successfully evades all the considered defenses, highlighting the critical need for new defensive mechanisms against targeted attacks in FL for autonomous driving.
• Combining multiple defensive strategies, as demonstrated by the LossF usion defense, can enhance the robustness against untargeted attacks.

The paper emphasizes the significant threat posed by backdoor attacks in FL and calls for the development of effective detection methods and ensemble techniques to improve defenses against targeted attacks.

Thống kê

"Federated learning is susceptible to clients with malicious intent that may manipulate their local updates before sending it to the server, so-called poisoning attacks."
"We demonstrate the inability of common defense mechanism to mitigate the Off-Track Attack, highlighting the critical need for new defensive mechanisms against targeted attacks within FL for autonomous driving."

Trích dẫn

"Poisoning attacks on FL are commonly tailored towards classification problems with only a small number targeting regression problems. However, regression tasks are common in autonomous driving, e.g., vehicle speed prediction, distance estimation, time-to-collision prediction, and vehicle trajectory prediction."
"Notably, common defense mechanism are largely inefficient against OTA."

Thông tin chi tiết chính được chắt lọc từ

Poisoning Attacks on Federated Learning for Autonomous Driving

by Sona... lúc arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01073.pdf

Yêu cầu sâu hơn

How can the proposed attacks be extended to other regression tasks in autonomous driving beyond vehicle trajectory prediction

The proposed attacks, FLStealth and Off-Track Attack (OTA), can be extended to other regression tasks in autonomous driving by adapting the attack strategies to suit different prediction objectives. For instance, in tasks like vehicle speed prediction, distance estimation, or time-to-collision prediction, FLStealth can be modified to target the specific features and parameters relevant to each task. Similarly, OTA can be tailored to inject triggers that manipulate the predicted outcomes in a way that is detrimental to the overall model performance for these tasks. By customizing the attacks based on the unique characteristics of each regression task, the vulnerabilities in federated learning models can be exposed and mitigated effectively.

What are the potential limitations of the current defense strategies, and how can they be improved to better handle both untargeted and targeted poisoning attacks in federated learning

The current defense strategies in federated learning have certain limitations that can be addressed to better handle both untargeted and targeted poisoning attacks. One potential limitation is the reliance on similarity-based defenses, such as robust aggregation techniques, which may not effectively detect sophisticated attacks like FLStealth or OTA. To improve defense strategies, a combination of anomaly detection methods and robust aggregation techniques can be implemented. Anomaly detection can help identify malicious behavior in individual clients, while robust aggregation can mitigate the impact of outliers in the model updates. Additionally, incorporating secure and privacy-preserving mechanisms in the federated learning process can enhance the overall security posture against poisoning attacks. Regular monitoring and auditing of model updates can also help in detecting and mitigating adversarial activities in real-time.

What other types of adversarial attacks, beyond poisoning, could be explored in the context of federated learning for autonomous driving applications

Beyond poisoning attacks, other types of adversarial attacks that could be explored in the context of federated learning for autonomous driving applications include model inversion attacks, membership inference attacks, and model extraction attacks. In model inversion attacks, adversaries attempt to reconstruct sensitive information from the model's outputs, posing a threat to data privacy. Membership inference attacks aim to determine whether a specific data point was used in the training of the model, compromising the confidentiality of the training data. Model extraction attacks involve extracting the architecture or parameters of the model, leading to intellectual property theft or model replication. By investigating and addressing these diverse adversarial threats, federated learning systems can be strengthened to ensure robust security and privacy protections in autonomous driving scenarios.