A Connected and Autonomous Vehicle Reference Architecture for Attack Surface Analysis

Maple, Carsten; Bradbury, Matthew; Le, Anhtuan; Ghirardello, Kevin

doi:10.3390/app9235101

Cited by 46 publications

(35 citation statements)

References 45 publications

(53 reference statements)

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“…They concluded that 3D point cloud classifiers, such as PointNet [44] or PointNet++ [45] are weak to adversaries leading to a high attack success rate. Another possible target could also be the algorithms utilized for the processing of the data (e.g., repair mechanic installing hacking device [46]). To analyze all possible scenarios, we can divide the threat models into two categories.…”

Section: A Adversarial Threat Modelmentioning

confidence: 99%

Countering Adversarial Attacks on Autonomous Vehicles Using Denoising Techniques: A Review

Kloukiniotis

Papandreou

Lalos

et al. 2022

IEEE Open J. Intell. Transp. Syst.

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The evolution of automotive technology will eventually permit the automated driving system on the vehicle to handle all circumstances. Human occupants will be just passengers. This poses security issues that need to be addressed. This paper has two aims. The first one investigates strategies for robustifying scene analysis of adversarial road scenes. A taxonomy of the defense mechanisms for countering adversarial perturbations is initially presented, classifying those mechanisms in three major categories: those that modify the data, those that propose adding extra models, and those that focus on modifying the models deployed for scene analysis. Motivated by the limited number of surveys in the first category, we further analyze the approaches that utilize input transformation operations as countermeasures, further classifying them in supervised and unsupervised methods and highlighting both their strengths and weaknesses. The second aim of this paper is to publish CarlaScenes dataset produced using the CARLA simulator. An extensive evaluation study, on CarlaScenes, is performed testing the supervised deep learning approaches that have been either proposed for image restoration or adversarial noise removal. The study presents insights on the robustness of the aforementioned approaches in mitigating adversarial attacks in scene analysis operations.INDEX TERMS Countering adversarial images, robust road scene analysis, deep learning.

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Section: A Adversarial Threat Modelmentioning

confidence: 99%

Countering Adversarial Attacks on Autonomous Vehicles Using Denoising Techniques: A Review

Kloukiniotis

Papandreou

Lalos

et al. 2022

IEEE Open J. Intell. Transp. Syst.

View full text Add to dashboard Cite

show abstract

“…A very productive approach to threats analysis is to consider the system as a whole, identify possible parts of it and then discover security problems that might be associated with each part of the system. This is done, for example, in the paper [ 9 ], exploring the vulnerabilities of connected and autonomous vehicles (CAV) and proposing a reference CAV architecture tailored for attack surface analysis.…”

Section: Related Workmentioning

confidence: 99%

In-Vehicle Situation Monitoring for Potential Threats Detection Based on Smartphone Sensors

Kashevnik

Ponomarev

Shilov

et al. 2020

Sensors

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This paper presents an analysis of modern research related to potential threats in a vehicle cabin, which is based on situation monitoring during vehicle control and the interaction of the driver with intelligent transportation systems (ITS). In the modern world, such systems enable the detection of potentially dangerous situations on the road, reducing accident probability. However, at the same time, such systems increase vulnerabilities in vehicles and can be sources of different threats. In this paper, we consider the primary information flows between the driver, vehicle, and infrastructure in modern ITS, and identify possible threats related to these entities. We define threat classes related to vehicle control and discuss which of them can be detected by smartphone sensors. We present a case study that supports our findings and shows the main use cases for threat identification using smartphone sensors: Drowsiness, distraction, unfastened belt, eating, drinking, and smartphone use.

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“…At the same time, Figure 1 refers to the sections of our work which apply the respective analysis. With a general focus on the IoV location service application (see Section III-A), we leverage sub-architectures, defined in the work [10], to model IoV system assets. This measure reduces the complexity and facilitates our security analysis.…”

Section: Introductionmentioning

confidence: 99%

Quantitative System-Level Security Verification of the IoV Infrastructure

Lauinger¹,

Aslam²,

Hamad³

et al. 2021

Preprint

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The Internet of Vehicles (IoV) equips vehicles with connectivity to the Internet and the Internet of Things (IoT) to support modern applications such as autonomous driving. However, the consolidation of complex computing domains of vehicles, the Internet, and the IoT limits the applicability of tailored security solutions. In this paper, we propose a new methodology to quantitatively verify the security of single or system-level assets of the IoV infrastructure. In detail, our methodology decomposes assets of the IoV infrastructure with the help of reference sub-architectures and the 4+1 view model analysis to map identified assets into data, software, networking, and hardware categories. This analysis includes a custom threat modeling concept to perform parameterization of Common Vulnerability Scoring System (CVSS) scores per view model domain. As a result, our methodology is able to allocate assets from attack paths to view model domains. This equips assets of attack paths with our IoV-driven CVSS scores. Our CVSS scores assess the attack likelihood which we use for Markov Chain transition probabilities. This way, we quantitatively verify system-level security among a set of IoV assets. Our results show that our methodology applies to arbitrary IoV attack paths. Based on our parameterization of CVSS scores and our selection of use cases, remote attacks are less likely to compromise location data compared to attacks from close proximity for authorized and unauthorized attackers respectively.

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A Connected and Autonomous Vehicle Reference Architecture for Attack Surface Analysis

Cited by 46 publications

References 45 publications

Countering Adversarial Attacks on Autonomous Vehicles Using Denoising Techniques: A Review

Countering Adversarial Attacks on Autonomous Vehicles Using Denoising Techniques: A Review

In-Vehicle Situation Monitoring for Potential Threats Detection Based on Smartphone Sensors

Quantitative System-Level Security Verification of the IoV Infrastructure

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