SoK: Rethinking Sensor Spoofing Attacks against Robotic Vehicles from a Systematic View

Xu, Yangsheng; Han, Xingshuo; Deng, Gelei; Li, Guanlin; Liu, Yang; Li, Jiwei; Zhang, Tianwei

doi:10.48550/arxiv.2205.04662

Cited by 4 publications

(5 citation statements)

References 66 publications

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“…Navigational attacks can manifest in various forms, ranging from simple and localized disruptions to controlled, sophisticated, widespread manipulations. One of the most common forms of attack involves sensor spoofing or jamming [95], [96], where adversaries intentionally deceive the robot's sensors, such as GPS, camera, LiDAR, etc, to provide false or distorted information. Doing so can mislead the robot's perception of its environment, leading to potential collisions, misrouting, or compromised decision-making.…”

Section: ) Navigation and Planning Attacks With Security Strategiesmentioning

confidence: 99%

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Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities

Neupane,

Mitra,

Fernandez

et al. 2024

IEEE Access

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Robotics and Artificial Intelligence (AI) have been inextricably intertwined since their inception. Today, AI-Robotics systems have become an integral part of our daily lives, from robotic vacuum cleaners to semi-autonomous cars. These systems are built upon three fundamental architectural elements: perception, navigation and planning, and control. However, while the integration of AI in Robotics systems has enhanced the quality of our lives, it has also presented a serious problem -these systems are vulnerable to security attacks. The physical components, algorithms, and data that makeup AI-Robotics systems can be exploited by malicious actors, potentially leading to dire consequences. Motivated by the need to address the security concerns in AI-Robotics systems, this paper presents a comprehensive survey and taxonomy across three dimensions: attack surfaces, ethical and legal concerns, and Human-Robot Interaction (HRI) security. Our goal is to provide readers, developers and other stakeholders with a holistic understanding of these areas to enhance the overall AI-Robotics system security. We begin by identifying potential attack surfaces and provide mitigating defensive strategies. We then delve into ethical issues, such as dependency and psychological impact, as well as the legal concerns regarding accountability for these systems. Besides, emerging trends such as HRI are discussed, considering privacy, integrity, safety, trustworthiness, and explainability concerns. Finally, we present our vision for future research directions in this dynamic and promising field.

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Section: ) Navigation and Planning Attacks With Security Strategiesmentioning

confidence: 99%

“…[90], [95], [109]- [111] Black-box DRL models and planners (Image Segmentation, 3-D point cloud, etc,)…”

Section: ) Navigation and Planning Attacks With Security Strategiesmentioning

confidence: 99%

Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities

Neupane,

Mitra,

Fernandez

et al. 2024

IEEE Access

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“…The target could be sensors like GPS, which could be spoofed for adversarial location [56], [57]. Similarly, LiDARs and cameras could be spoofed for false detection and tracking of objects [86]. Inexpensive equipment can be used to spoof the sensors and eventually carry different types of attacks, including replay and relay attacks [61], [87].…”

Section: ) Spoofingmentioning

confidence: 99%

Toward Attack Modeling Technique Addressing Resilience in Self-Driving Car

et al. 2023

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Self-driving cars are going to be the main future mode of transportation. However, such systems like, any other cyber-physical system, are vulnerable to attack vectors and uncertainties. As a response, resilience-based approaches are being developed. However, the approaches lack a sound attack model that recognizes the attack vectors and vulnerabilities such a system would have and that does a proper severity analysis of such attacks. Moreover, the existing attack models are too generic. Currently, the domain lacks such specific work pertaining to self-driving cars. Given the technology and architecture of self-driving cars, the field requires a domain-specific attack model. This paper gives a review of the attack models and proposes a domain-specific attack model for self-driving cars. The proposed attack model, severity-based analytical attack model for resilience (SAAMR), provides attack analysis based on existing models. Also, a domainbased severity score for attacks is calculated. Further, the attacks are classified using the decision-tree method and predictions of the type of attacks are given using long short-term memory network. INDEX TERMSAttack-model, autonomous vehicles, cyber-attacks, resilience, security, self-driving car. NOMENCLATURE AT Adversarial attack tree. CAN Controller area network. CT Code modification/injection tree. CVE Common vulnerabilities and exposures. CVSS Common vulnerability scoring system. CWE Common weakness enumeration. DATMO Detection and tracking of moving objects. DDoS Distributed denial of service. DoS Denial of service. DT Decision tree. ECU Electronic controller unit. GPS Global positioning system. InV In-vehicle. ITS Intelligent transportation system. JT Jamming attack tree. LiDAR Light detection and ranging.

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“…For example, researchers in [86,87] have identified over 100 sensor spoofing attack vectors for vehicles.…”

Section: Security Impact On the Av Safetymentioning

confidence: 99%

A map-based model-driven testing framework for automated driving systems

Tang¹

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SoK: Rethinking Sensor Spoofing Attacks against Robotic Vehicles from a Systematic View

Cited by 4 publications

References 66 publications

Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities

Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities

Toward Attack Modeling Technique Addressing Resilience in Self-Driving Car

A map-based model-driven testing framework for automated driving systems

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