Cyber-physical security for smart cars

Humayed, Abdulmalik; Luo, Bo

doi:10.1145/2735960.2735992

Cited by 19 publications

(6 citation statements)

References 1 publication

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“…The authors of [ 17 ] classify the environment-vehicle-driver threats for cyber, physical and cyber-physical. They also represent a two-dimensional classification, where the other dimension is constituted of vulnerabilities, attacks and threats.…”

Section: Related Workmentioning

confidence: 99%

“…This section aims at threat classification with the purpose of defining which classes of threats can be detected via usage of smartphone. On this basis, the classification from [ 17 ] was used. In order to define sources and aims of threats, the vulnerabilities, attacks and threats that can take place in the Driver-Environment-DAS system were considered.…”

Section: Classification Of Potential Threats In Vehicle Cabinmentioning

confidence: 99%

“…Various cyber vulnerabilities (e.g., packet injection, malware injection, etc.) [13][14][15][16][17].…”

Section: Cybermentioning

confidence: 99%

“…Vulnerability to sensory channel attack (manipulating the physical environment to deceive vehicle's sensors) [16,[35][36][37], hardware tampering to infect with malware [16] or installing additional hardware (e.g., for eavesdropping [17]), insecure CAN bus [38], and other.…”

Section: Cyber-physicalmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Classification Of Potential Threats In Vehicle Cabinmentioning

confidence: 99%

“…Various cyber vulnerabilities (e.g., packet injection, malware injection, etc.) [13][14][15][16][17].…”

Section: Cybermentioning

confidence: 99%

Section: Cyber-physicalmentioning

confidence: 99%

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

Kashevnik

Ponomarev

Shilov

et al. 2020

Sensors

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“…So the security of CPS is one of the vital paradigms for this. But securing cyber-physical systems is also a challenging task due to its heterogeneity of components, complex interactions among cyber-physical systems, and the attack surface's complexities [2]. It is observed that an intruder can randomly interrupt the dynamism of systems or encourage agitations to cyber-physical systems deprived of the security of various strategies of hardware or software, which leads to substantial social victims or the lives of humans [3] [4] [5] [6] [7] [8] [9].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Analysis of Enhancing Cyber Security Using Deep Learning for Cyber Physical Systems

Gaba,

Budhiraja,

Kumar

et al. 2024

IEEE Access

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In this current era, cyber-physical systems (CPSs) have gained concentrated consideration in various fields because of their emergent applications. Though the robust dependence on communication networks creates cyber-physical systems susceptible to deliberated cyber related attacks and detecting these cyber-attacks are the most challenging task. There is the interaction among the components of the cyber and physical worlds, so CPS security needs a distinct approach from past security concerns. Deep learning (DL) distributes better performance than machine learning (ML) due to its layered architecture and the efficient algorithm for extracting prominent information from training data. So, the deep learning models are taken into consideration quickly for detecting cyber-attacks in cyber physical systems. As numerous attack detection methods have been proposed by various authors for enforcing CPS security, this paper reviews and analyzes multiple ways of attack detection presented for CPS using deep learning. We will be putting the excellent potential for detecting cyber-attacks for CPS concerning deep learning modules. The admirable performance is attained partly as highly quality datasets are eagerly obtainable for the use of the public. Moreover, various challenges and research inclinations are also discussed in impending research.

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