A taxonomy of attack mechanisms in the automotive domain

Pekaric, Irdin; Sauerwein, Clemens; Haselwanter, Stefan; Felderer, Michael

doi:10.1016/j.csi.2021.103539

“…Side-channel attacks are typically timing attacks, power monitoring attacks, electromagnetic leaks, acoustic signals, transient characteristics, or data remanence at hardware or software levels [111], [112]. Le et al [18] explained reliance on obscurity for security, bad programming practices, lack of cryptographic-or insufficient hardware protection can cause side-channel attacks affecting critical devices like ECUs or V2X communications.…”

Section: Defenses Against Side-channel Attackmentioning

confidence: 99%

An investigation of cyber-attacks and security mechanisms for connected and autonomous vehicles

Gupta¹,

Maple²

2023

Preprint

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<p>Edge Computing-based Connected Autonomous Vehicles (EC-CAVs) can fulfill the emerging demand for smart transportation on a global scale. Such innovations to transportation ecosystems can bring manyfold benefits from fully autonomous driving services to proactive vehicle monitoring and traffic management. Given the complexity involved in the deployment of EC-CAVs, zero-tolerance safety and security measures must be incorporated to avert vehicle immobilization, road accidents, disclosure of sensitive data, or any potential threats. This paper presents a reference architecture of EC-CAVs to investigate existing and emerging cyber-threats for deriving a common attack taxonomy for the EC-CAVs ecosystem. Subsequently, a comprehensive survey of security mechanisms is presented that can ensure safe and secure transportation of passengers from one destination to another using an EC-CAVs ecosystem.</p>

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“…Side-channel attacks are typically timing attacks, power monitoring attacks, electromagnetic leaks, acoustic signals, transient characteristics, or data remanence at hardware or software levels [111,112]. Le et al [15] explained reliance on obscurity for security, bad programming practices, lack of cryptographic-or insufficient hardware protection can cause side-channel attacks affecting critical devices like ECUs or V2X communications.…”

Section: Defenses Against Side-channel Attackmentioning

confidence: 99%

An investigation of cyber-attacks and security mechanisms for connected and autonomous vehicles

Gupta¹,

Maple²

2023

Preprint

0

View full text Add to dashboard Cite

<p>Edge Computing-based Connected Autonomous Vehicles (EC-CAVs) can fulfill the emerging demand for smart transportation on a global scale. Such innovations to transportation ecosystems can bring manyfold benefits from fully autonomous driving services to proactive vehicle monitoring and traffic management. Given the complexity involved in the deployment of EC-CAVs, zero-tolerance safety and security measures must be incorporated to avert vehicle immobilization, road accidents, disclosure of sensitive data, or any potential threats. This paper presents a reference architecture of EC-CAVs to investigate existing and emerging cyber-threats for deriving a common attack taxonomy for the EC-CAVs ecosystem. Subsequently, a comprehensive survey of security mechanisms is presented that can ensure safe and secure transportation of passengers from one destination to another using an EC-CAVs ecosystem.</p>

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“…Eq. ( 11), (12), and ( 13) describe the mathematical operation. Here, k > 0, k ϵ are two features with the index of k.…”

Section: √∑ (mentioning

confidence: 99%

“…Penny [11] introduced a man-in-the-middle attack or relay attack, to do this radio signals are passed between two devices. Pekaric I et al (2021) [12] described other attacks such as GPS spoofing and message injection attacks. BMW Connected Drive [13] seamlessly integrates mobile devices, smart home technology, and vehicle's intelligent interfaces into a complete driver's environment.…”

Section: Introductionmentioning

confidence: 99%

A Novel Compound Feature based Driver Identification

Khan¹,

Ali²,

Fazlul³

et al. 2022

IJACSA

2

0

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In today's world, it is time to identify the driver through technology. At present, it is possible to find out the driving style of the drivers from every car through controller area network (CAN-BUS) sensor data which was not possible through the conventional car. Many researchers did their work and their main purpose was to find out the driver driving style from end-to-end analysis of CAN-BUS sensor data. So, it is potential to identify each driver individually based on the driver's driving style. We propose a novel compound featurebased driver identification to reduce the number of input attributes based on some mathematical operation. Now, the role of machine learning in the field of any type of data analysis is incomparable and significant. The state-of-the-art algorithms have been applied in different fields. Occasionally these are tested in a similar domain. As a result, we have used some prominent algorithms of machine learning, which show different results in the field of aspiration of the model. The other goal of this study is to compare the conspicuous classification algorithms in the index of performance metrics in driver behavior identification. Hence, we compare the performance of SVM, Naïve Bayes, Logistic Regression, k-NN, Random Forest, Decision tree, Gradient boosting.

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A taxonomy of attack mechanisms in the automotive domain

Cited by 27 publications

References 23 publications

An investigation of cyber-attacks and security mechanisms for connected and autonomous vehicles

An investigation of cyber-attacks and security mechanisms for connected and autonomous vehicles

An investigation of cyber-attacks and security mechanisms for connected and autonomous vehicles

A Novel Compound Feature based Driver Identification

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