Poisoning Attacks on Federated Learning for Autonomous Driving

• URL: http://arxiv.org/abs/2405.01073v1
• Date: Thu, 2 May 2024 08:06:10 GMT
• Title: Poisoning Attacks on Federated Learning for Autonomous Driving
• Authors: Sonakshi Garg, Hugo Jönsson, Gustav Kalander, Axel Nilsson, Bhhaanu Pirange, Viktor Valadi, Johan Östman,
• Abstract summary: We introduce two novel poisoning attacks tailored to regression tasks within autonomous driving: FLStealth and Off-Track Attack (OTA) OTA is a targeted attack with the objective to change the global model's behavior when exposed to a certain trigger. In particular, we show that, among five different untargeted attacks, FLStealth is the most successful at bypassing the considered defenses employed by the server.
• Score: 2.372971341944509
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) is a decentralized learning paradigm, enabling parties to collaboratively train models while keeping their data confidential. Within autonomous driving, it brings the potential of reducing data storage costs, reducing bandwidth requirements, and to accelerate the learning. FL is, however, susceptible to poisoning attacks. In this paper, we introduce two novel poisoning attacks on FL tailored to regression tasks within autonomous driving: FLStealth and Off-Track Attack (OTA). FLStealth, an untargeted attack, aims at providing model updates that deteriorate the global model performance while appearing benign. OTA, on the other hand, is a targeted attack with the objective to change the global model's behavior when exposed to a certain trigger. We demonstrate the effectiveness of our attacks by conducting comprehensive experiments pertaining to the task of vehicle trajectory prediction. In particular, we show that, among five different untargeted attacks, FLStealth is the most successful at bypassing the considered defenses employed by the server. For OTA, we demonstrate the inability of common defense strategies to mitigate the attack, highlighting the critical need for new defensive mechanisms against targeted attacks within FL for autonomous driving.

Related papers

• Celtibero: Robust Layered Aggregation for Federated Learning [0.0]
  We introduce Celtibero, a novel defense mechanism that integrates layered aggregation to enhance robustness against adversarial manipulation. We demonstrate that Celtibero consistently achieves high main task accuracy (MTA) while maintaining minimal attack success rates (ASR) across a range of untargeted and targeted poisoning attacks.
  arXiv  Detail & Related papers  (2024-08-26T12:54:00Z)
• FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning [98.43475653490219]
  Federated learning (FL) is susceptible to poisoning attacks. FreqFed is a novel aggregation mechanism that transforms the model updates into the frequency domain. We demonstrate that FreqFed can mitigate poisoning attacks effectively with a negligible impact on the utility of the aggregated model.
  arXiv  Detail & Related papers  (2023-12-07T16:56:24Z)
• Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach [65.2993866461477]
  This paper proposes a data-agnostic, model poisoning attack on Federated Learning (FL) The attack requires no knowledge of FL training data and achieves both effectiveness and undetectability. Experiments show that the FL accuracy drops gradually under the proposed attack and existing defense mechanisms fail to detect it.
  arXiv  Detail & Related papers  (2023-11-30T12:19:10Z)
• SPFL: A Self-purified Federated Learning Method Against Poisoning Attacks [12.580891810557482]
  Federated learning (FL) is attractive for pulling privacy-preserving distributed training data. We propose a self-purified FL (SPFL) method that enables benign clients to exploit trusted historical features of locally purified model. We experimentally demonstrate that SPFL outperforms state-of-the-art FL defenses against various poisoning attacks.
  arXiv  Detail & Related papers  (2023-09-19T13:31:33Z)
• Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations [55.2480439325792]
  Federated Learning (FL) facilitates decentralized machine learning model training, preserving data privacy, lowering communication costs, and boosting model performance through diversified data sources. FL faces vulnerabilities such as poisoning attacks, undermining model integrity with both untargeted performance degradation and targeted backdoor attacks. We define a new notion of strong adaptive adversaries, capable of adapting to multiple objectives simultaneously. MESAS is the first defense robust against strong adaptive adversaries, effective in real-world data scenarios, with an average overhead of just 24.37 seconds.
  arXiv  Detail & Related papers  (2023-06-06T11:44:42Z)
• AdvDO: Realistic Adversarial Attacks for Trajectory Prediction [87.96767885419423]
  Trajectory prediction is essential for autonomous vehicles to plan correct and safe driving behaviors. We devise an optimization-based adversarial attack framework to generate realistic adversarial trajectories. Our attack can lead an AV to drive off road or collide into other vehicles in simulation.
  arXiv  Detail & Related papers  (2022-09-19T03:34:59Z)
• Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDS [70.60975663021952]
  We study blackbox adversarial attacks on network classifiers. We argue that attacker-defender fixed points are themselves general-sum games with complex phase transitions. We show that a continual learning approach is required to study attacker-defender dynamics.
  arXiv  Detail & Related papers  (2021-11-23T23:42:16Z)
• FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective [35.10520095377653]
  Federated learning (FL) is a popular distributed learning framework that trains a global model through iterative communications between a central server and edge devices. Recent works have demonstrated that FL is vulnerable to model poisoning attacks. We propose a client-based defense, named White Blood Cell for Federated Learning (FL-WBC), which can mitigate model poisoning attacks.
  arXiv  Detail & Related papers  (2021-10-26T17:13:35Z)
• Adversarial defense for automatic speaker verification by cascaded self-supervised learning models [101.42920161993455]
  More and more malicious attackers attempt to launch adversarial attacks at automatic speaker verification (ASV) systems. We propose a standard and attack-agnostic method based on cascaded self-supervised learning models to purify the adversarial perturbations. Experimental results demonstrate that the proposed method achieves effective defense performance and can successfully counter adversarial attacks.
  arXiv  Detail & Related papers  (2021-02-14T01:56:43Z)
• Untargeted Poisoning Attack Detection in Federated Learning via Behavior Attestation [7.979659145328856]
  Federated Learning (FL) is a paradigm in Machine Learning (ML) that addresses data privacy, security, access rights and access to heterogeneous information issues. Despite its advantages, there is an increased potential for cyberattacks on FL-based ML techniques that can undermine the benefits. We propose attestedFL, a defense mechanism that monitors the training of individual nodes through state persistence in order to detect a malicious worker.
  arXiv  Detail & Related papers  (2021-01-24T20:52:55Z)
• Dynamic backdoor attacks against federated learning [0.5482532589225553]
  Federated Learning (FL) is a new machine learning framework, which enables millions of participants to collaboratively train model without compromising data privacy and security. In this paper, we focus on dynamic backdoor attacks under FL setting, where the goal of the adversary is to reduce the performance of the model on targeted tasks. To the best of our knowledge, this is the first paper that focus on dynamic backdoor attacks research under FL setting.
  arXiv  Detail & Related papers  (2020-11-15T01:32:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.