On Data Fabrication in Collaborative Vehicular Perception: Attacks and Countermeasures

• URL: http://arxiv.org/abs/2309.12955v2
• Date: Tue, 3 Oct 2023 17:48:40 GMT
• Title: On Data Fabrication in Collaborative Vehicular Perception: Attacks and Countermeasures
• Authors: Qingzhao Zhang, Shuowei Jin, Ruiyang Zhu, Jiachen Sun, Xumiao Zhang, Qi Alfred Chen, Z. Morley Mao
• Abstract summary: Collaborative perception, which greatly enhances the sensing capability of connected and autonomous vehicles (CAVs), brings forth potential security risks. We propose various real-time data fabrication attacks in which the attacker delivers crafted malicious data to victims in order to perturb their perception results. Our attacks demonstrate a high success rate of over 86% on high-fidelity simulated scenarios and are realizable in real-world experiments. We present a systematic anomaly detection approach that enables benign vehicles to jointly reveal malicious fabrication.
• Score: 22.338269462708368
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Collaborative perception, which greatly enhances the sensing capability of connected and autonomous vehicles (CAVs) by incorporating data from external resources, also brings forth potential security risks. CAVs' driving decisions rely on remote untrusted data, making them susceptible to attacks carried out by malicious participants in the collaborative perception system. However, security analysis and countermeasures for such threats are absent. To understand the impact of the vulnerability, we break the ground by proposing various real-time data fabrication attacks in which the attacker delivers crafted malicious data to victims in order to perturb their perception results, leading to hard brakes or increased collision risks. Our attacks demonstrate a high success rate of over 86% on high-fidelity simulated scenarios and are realizable in real-world experiments. To mitigate the vulnerability, we present a systematic anomaly detection approach that enables benign vehicles to jointly reveal malicious fabrication. It detects 91.5% of attacks with a false positive rate of 3% in simulated scenarios and significantly mitigates attack impacts in real-world scenarios.

Related papers

• Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense [3.685395311534351]
  Federated Learning (FL) is a distributed machine learning diagram that enables multiple clients to collaboratively train a global model without sharing their private local data. FL systems are vulnerable to attacks that are happening in malicious clients through data poisoning and model poisoning. Existing defense methods typically focus on mitigating specific types of poisoning and are often ineffective against unseen types of attack.
  arXiv  Detail & Related papers  (2024-08-05T20:27:45Z)
• Mellivora Capensis: A Backdoor-Free Training Framework on the Poisoned Dataset without Auxiliary Data [29.842087372804905]
  This paper addresses the challenges of backdoor attack countermeasures in real-world scenarios. We propose a robust and clean-data-free backdoor defense framework, namely Mellivora Capensis (textttMeCa), which enables the model trainer to train a clean model on the poisoned dataset.
  arXiv  Detail & Related papers  (2024-05-21T12:20:19Z)
• Enabling Privacy-Preserving Cyber Threat Detection with Federated Learning [4.475514208635884]
  This study systematically profiles the (in)feasibility of learning for privacy-preserving cyber threat detection in terms of effectiveness, byzantine resilience, and efficiency. It shows that FL-trained detection models can achieve a performance that is comparable to centrally trained counterparts. Under a realistic threat model, FL turns out to be adversary-resistant to attacks of both data poisoning and model poisoning.
  arXiv  Detail & Related papers  (2024-04-08T01:16:56Z)
• Eroding Trust In Aerial Imagery: Comprehensive Analysis and Evaluation Of Adversarial Attacks In Geospatial Systems [24.953306643091484]
  We show how adversarial attacks can degrade confidence in geospatial systems. We empirically show their threat to remote sensing systems using high-quality SpaceNet datasets.
  arXiv  Detail & Related papers  (2023-12-12T16:05:12Z)
• 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)
• Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study [79.33449311057088]
  This paper empirically reveals the high vulnerability of 11 typical incremental learners against poisoning-based backdoor attack on 3 learning scenarios. The defense mechanism based on activation clustering is found to be effective in detecting our trigger pattern to mitigate potential security risks.
  arXiv  Detail & Related papers  (2023-05-28T09:17:48Z)
• Robust Trajectory Prediction against Adversarial Attacks [84.10405251683713]
  Trajectory prediction using deep neural networks (DNNs) is an essential component of autonomous driving systems. These methods are vulnerable to adversarial attacks, leading to serious consequences such as collisions. In this work, we identify two key ingredients to defend trajectory prediction models against adversarial attacks.
  arXiv  Detail & Related papers  (2022-07-29T22:35:05Z)
• Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks [76.35478518372692]
  We introduce epsilon-illusory, a novel form of adversarial attack on sequential decision-makers. Compared to existing attacks, we empirically find epsilon-illusory to be significantly harder to detect with automated methods. Our findings suggest the need for better anomaly detectors, as well as effective hardware- and system-level defenses.
  arXiv  Detail & Related papers  (2022-07-20T19:49:09Z)
• RobustSense: Defending Adversarial Attack for Secure Device-Free Human Activity Recognition [37.387265457439476]
  We propose a novel learning framework, RobustSense, to defend common adversarial attacks. Our method works well on wireless human activity recognition and person identification systems.
  arXiv  Detail & Related papers  (2022-04-04T15:06:03Z)
• Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning [51.15273664903583]
  Data heterogeneity has been identified as one of the key features in federated learning but often overlooked in the lens of robustness to adversarial attacks. This paper focuses on characterizing and understanding its impact on backdooring attacks in federated learning through comprehensive experiments using synthetic and the LEAF benchmarks.
  arXiv  Detail & Related papers  (2021-02-01T06:06:21Z)
• Epidemic mitigation by statistical inference from contact tracing data [61.04165571425021]
  We develop Bayesian inference methods to estimate the risk that an individual is infected. We propose to use probabilistic risk estimation in order to optimize testing and quarantining strategies for the control of an epidemic. Our approaches translate into fully distributed algorithms that only require communication between individuals who have recently been in contact.
  arXiv  Detail & Related papers  (2020-09-20T12:24:45Z)

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.