Towards a Security Architecture for Protecting Connected Vehicles from Malware

Iqbal, Shahrear; Haque, Anwar; Zulkernine, Mohammad

doi:10.1109/vtcspring.2019.8746516

Cited by 16 publications

(12 citation statements)

References 13 publications

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“…However, softwares are never perfect. It is commonly assumed the existence of many vulnerabilities that can be exploited to cause unexpected behaviors using malware leading to life-threatening situations [100]. Software vulnerabilities are caused by software errors and flaws introduced during the design or implementation phases.…”

Section: ) Software Vulnerabilitiesmentioning

confidence: 99%

“…For the implementation of communication protocols, hardware drivers, as well as user applications, modern vehicular software could have more than 100 million lines of code exposed to all kinds of software vulnerabilities [100]. This gives opportunities for attackers to design effective malware to gain unauthorized access and disrupt the regular functioning of vehicles.…”

Section: C: Malware Attackmentioning

confidence: 99%

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A Survey on the Current Security Landscape of Intelligent Transportation Systems

Lamssaggad¹,

Benamar²,

Hafid³

et al. 2021

IEEE Access

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With the proliferation of embedded technologies and wireless capabilities, today's vehicles are no longer isolated mechanical machines. They become part of a hyper-connected system-Intelligent Transportation Systems (ITS)-that has the potential to support multiple levels of autonomy and intelligence improving considerably the safety, efficiency, and sustainability of transportation networks. However, this raises new security issues that make the whole system prone to cybersecurity attacks that threaten both the safety and privacy of all road-users. This paper gives a short background tutorial on the main security issues and the different attacks that hinder Intelligent Transport Systems. To enable secure and safe ITS applications, this paper provides a comprehensive analysis of existing solutions and highlights their strengths and limitations. Finally, this survey presents key challenges in the field, and discusses recent trends that must be factored in by researchers, implementers, and car manufactures to improve the security of ITS.

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Section: ) Software Vulnerabilitiesmentioning

confidence: 99%

Section: C: Malware Attackmentioning

confidence: 99%

A Survey on the Current Security Landscape of Intelligent Transportation Systems

Lamssaggad¹,

Benamar²,

Hafid³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

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“…The increased internet connectivity offered by vehicles has led to challenges in securing the vehicular network as the attack surface area increases [20]. Modern vehicles allow devices to be connected via infotainment systems, creating a pathway to inject malware compromising ECU leading to unforeseen consequences.…”

Section: Literature Surveymentioning

confidence: 99%

“…Modern vehicles allow devices to be connected via infotainment systems, creating a pathway to inject malware compromising ECU leading to unforeseen consequences. A framework for vehicular malware characterization and protection has been proposed in [20]. The framework uses virtualization to reduce the vehicular attack surface.…”

Section: Literature Surveymentioning

confidence: 99%

Evolution of VANETS to IoV

Lone

Verma

Sharma

2021

Teh. glas. (Online)

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Advancement in wireless communication technology along with the evolution of low power computational devices, have given rise to the Internet of things paradigm. This paradigm is transforming conventional VANETs into Internet-of- vehicles. This transition has led to a substantial commercial interest; as a result, there has been a significant boost in the field of the Internet of vehicles during the past few years. IoV promises a wide range of applications of commercial interest as well as public entertainment and convenience (collision warning systems, on-demand in-car entertainment, smart parking, traffic information). Applications related to vehicular and passenger safety are particularly of great commercial as well as a research interest as such IoV is going to be a core component in implementing the smart city concept. This paper gives an overview of the transition of conventional VANETs to IoV and highlights the potential applications and challenges faced by the Internet of Vehicles (IoV) paradigm.

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“…When the gateway has the proposed IDS module, it can predict and detect malware attacks from external ports. have reported that malware can be detected in the network tra c of devices [18,19]. is paper selected nine features using the IFS method and shows that malware can be detected from the network tra c using a machine learning-based IDS module.…”

Section: Intrusion Detection Modulementioning

confidence: 99%

Malware Detection in Self-Driving Vehicles Using Machine Learning Algorithms

Park

Choi

2020

Journal of Advanced Transportation

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The recent trend for vehicles to be connected to unspecified devices, vehicles, and infrastructure increases the potential for external threats to vehicle cybersecurity. Thus, intrusion detection is a key network security function in vehicles with open connectivity, such as self-driving and connected cars. Specifically, when a vehicle is connected to an external device through a smartphone inside the vehicle or when a vehicle communicates with external infrastructure, security technology is required to protect the software network inside the vehicle. Existing technology with this function includes vehicle gateways and intrusion detection systems. However, it is difficult to block malicious code based on application behaviors. In this study, we propose a machine learning-based data analysis method to accurately detect abnormal behaviors due to malware in large-scale network traffic in real time. First, we define a detection architecture, which is required by the intrusion detection module to detect and block malware attempting to affect the vehicle via a smartphone. Then, we propose an efficient algorithm for detecting malicious behaviors in a network environment and conduct experiments to verify algorithm accuracy and cost through comparisons with other algorithms.

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Towards a Security Architecture for Protecting Connected Vehicles from Malware

Cited by 16 publications

References 13 publications

A Survey on the Current Security Landscape of Intelligent Transportation Systems

A Survey on the Current Security Landscape of Intelligent Transportation Systems

Evolution of VANETS to IoV

Malware Detection in Self-Driving Vehicles Using Machine Learning Algorithms

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