Vulnerability-Oriented Fuzz Testing for Connected Autonomous Vehicle Systems

Moukahal, Lama J.; Zulkernine, Mohammad; Soukup, Martin

doi:10.1109/tr.2021.3112538

Cited by 23 publications

(8 citation statements)

References 37 publications

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“…As for automotive systems, Moukahal et al [ 55 ] designed a fuzz testing framework, VulFuzz. The framework uses security metrics to rank the security priority of automotive components and tests the most vulnerable components thoroughly.…”

Section: Automotive Cybersecurity Testing Methodsmentioning

confidence: 99%

Cybersecurity Testing for Automotive Domain: A Survey

Luo

Zhang

Yang

et al. 2022

Sensors

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Modern vehicles are more complex and interconnected than ever before, which also means that attack surfaces for vehicles have increased significantly. Malicious cyberattacks will not only exploit personal privacy and property, but also affect the functional safety of electrical/electronic (E/E) safety-critical systems by controlling the driving functionality, which is life-threatening. Therefore, it is necessary to conduct cybersecurity testing on vehicles to reveal and address relevant security threats and vulnerabilities. Cybersecurity standards and regulations issued in recent years, such as ISO/SAE 21434 and UNECE WP.29 regulations (R155 and R156), also emphasize the indispensability of cybersecurity verification and validation in the development lifecycle but lack specific technical details. Thus, this paper conducts a systematic and comprehensive review of the research and practice in the field of automotive cybersecurity testing, which can provide reference and advice for automotive security researchers and testers. We classify and discuss the security testing methods and testbeds in automotive engineering. Furthermore, we identify gaps and limitations in existing research and point out future challenges.

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Section: Automotive Cybersecurity Testing Methodsmentioning

confidence: 99%

Cybersecurity Testing for Automotive Domain: A Survey

Luo

Zhang

Yang

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Despite the amount of research on the topic, there is still a lack of adequate testing methods that can accommodate all possible situations an AV may face [73,[95][96][97][98]. According to [102], it is challenging to test AVs in a very comprehensive and safe manner.…”

Section: Challengesmentioning

confidence: 99%

“…[97] proposed a framework that utilizes signal temporal logic formulae and evaluates test cases against it for AV systems. In [98], the researchers applied a fuzzy testing method for AV systems that was aimed at testing the most vulnerable components. Essentially, the vehicle industry faces a host of safety challenges which can be prevented by adequate testing procedures.…”

Section: Metaverse In Data‐driven Intelligent Transportation Systemsmentioning

confidence: 99%

Prospects and challenges of Metaverse application in data‐driven intelligent transportation systems

Njoku

Nwakanma

Amaizu

et al. 2022

IET Intelligent Trans Sys

132

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The Metaverse is a concept used to refer to a virtual world that exists in parallel to the physical world. It has grown from a conceptual level to having real applications in virtual reality games. The applicability of the Metaverse in numerous sectors like marketing, education, social, and even advertising exists. However, there exists little or no work on Metaverse applicability to the transportation industry. Data‐driven intelligent transportation systems (DDITS) aim to provide more intelligent systems based on exploiting data. This paper reviews the concepts and features of the Metaverse. Also, the review goes over three dominant DDITS challenges: vehicle fault detection and repair, testing new technologies, and anti‐theft systems. In addition, it highlights prospective Metaverse solutions that apply to the DDITS. Buttressing the utility of Metaverse in DDITS, this paper presents two major case studies: the invisible to visible (I2V) and the Metaverse on Wheels (MoW) technologies. Finally, the influence, limitations, and open issues of Metaverse applications to DDITS are discussed.

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“…e proposed metrics were applied to OP to show how to measure the car's software. Another approach was also taken by Moukahal et al in Vulnerability-oriented fuzz (VulFuzz) testing to direct and prioritize the fuzz testing toward the most vulnerable components in connected cars [26]. To increase test performance and avoid dropped test cases, they used an input structureaware mutation technique to bypass car software systems' input formats.…”

Section: Related Workmentioning

confidence: 99%

Reliability and Security Analysis of Artificial Intelligence-Based Self-Driving Technologies in Saudi Arabia: A Case Study of Openpilot

Alsubaei

2022

Journal of Advanced Transportation

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Saudi Arabia has an ambitious vision that embraces artificial intelligence (AI) technologies at a mass scale in new cities such as Neom. Self-driving has recently become one of the most important AI applications due to the advancement of sensors and AI algorithms. Given that safety is vital to the success of self-driving cars, existing infrastructures (e.g., roads and traffic signs) should be compatible with self-driving technologies. However, self-driving technologies have not been thoroughly examined in Saudi Arabia with regard to the country’s infrastructure and traffic. Therefore, this paper highlights the main areas of improvement in available self-driving technologies in Saudi Arabia. This analysis can help governments understand the current limitations of such technologies so that they can regulate them and enhance infrastructures to prepare for the mass adoption of self-driving cars. It can also help car manufacturers and developers improve self-driving algorithms to overcome their existing limitations, which will ultimately improve the safety and experience of driving.

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Vulnerability-Oriented Fuzz Testing for Connected Autonomous Vehicle Systems

Cited by 23 publications

References 37 publications

Cybersecurity Testing for Automotive Domain: A Survey

Cybersecurity Testing for Automotive Domain: A Survey

Prospects and challenges of Metaverse application in data‐driven intelligent transportation systems

Reliability and Security Analysis of Artificial Intelligence-Based Self-Driving Technologies in Saudi Arabia: A Case Study of Openpilot

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