SafeShareRide: Edge-Based Attack Detection in Ridesharing Services

Liu, Liangkai; Zhang, Xingzhou; Qiao, Mu; Shi, Weisong

doi:10.1109/sec.2018.00009

Cited by 28 publications

(6 citation statements)

References 24 publications

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“…OpenVDAP is a full-stack edge-based data analytic platform for connected and autonomous vehicles (CAVs) [148]. It envisions the future four types of CAVs applications, including autonomous driving, in-vehicle infotainment, real-time diagnostics, and third-party applications like traffic information collector and SafeShareRide [150]. The hierarchical design of the storage system called driving data integrator (DDI) is proposed in OpenVDAP to provide sensor-aware and application-aware data storage and processing [148].…”

Section: E Storagementioning

confidence: 99%

Edge Computing for Autonomous Driving: Opportunities and Challenges

Liu¹,

et al. 2019

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The recent proliferation of computing technologies, e.g., sensors, computer vision, machine learning, hardware acceleration, and the broad deployment of communication mechanisms, e.g., DSRC, C-V2X, 5G, have pushed the horizon of autonomous driving, which automates the decision and control of vehicles by leveraging the perception results based on multiple sensors. The key to the success of these autonomous systems is making a reliable decision in a real-time fashion. However, accidents and fatalities caused by early deployed autonomous vehicles arise from time to time. The real traffic environment is too complicated for the current autonomous driving computing systems to understand and handle. In this paper, we present the state-of-the-art computing systems for autonomous driving, including seven performance metrics and nine key technologies, followed by eleven challenges and opportunities to realize autonomous driving. We hope this paper will gain attention from both the computing and automotive communities and inspire more research in this direction.

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Section: E Storagementioning

confidence: 99%

Edge Computing for Autonomous Driving: Opportunities and Challenges

Liu¹,

et al. 2019

Self Cite

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“…Smartphone systems' logs and traffic are often used for the anomaly [69] and attacks detection [70], including the detection of complex multistep attacks [71]. However, for the security and privacy analysis of the smartphone-based driver monitoring systems, they are mostly used for the analysis of the work with the user's sensitive data [72,73].…”

Section: Related Workmentioning

confidence: 99%

Security and Privacy Analysis of Smartphone-Based Driver Monitoring Systems from the Developer’s Point of View

Levshun

Chechulin

Kotenko

2022

Sensors

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Nowadays, the whole driver monitoring system can be placed inside the vehicle driver’s smartphone, which introduces new security and privacy risks to the system. Because of the nature of the modern transportation systems, the consequences of the security issues in such systems can be crucial, leading to threat to human life and health. Moreover, despite the large number of security and privacy issues discovered in smartphone applications on a daily basis, there is no general approach for their automated analysis that can work in conditions that lack data and take into account specifics of the application area. Thus, this paper describes an original approach for a security and privacy analysis of driver monitoring systems based on smartphone sensors. This analysis uses white-box testing principles and aims to help developers evaluate and improve their products. The novelty of the proposed approach lies in combining various security and privacy analysis algorithms into a single automated approach for a specific area of application. Moreover, the suggested approach is modular and extensible, takes into account specific features of smartphone-based driver monitoring systems and works in conditions of lack or inaccessibility of data. The practical significance of the approach lies in the suggestions that are provided based on the conducted analysis. Those suggestions contain detected security and privacy issues and ways of their mitigation, together with limitations of the analysis due to the absence of data. It is assumed that such an approach would help developers take into account important aspects of security and privacy, thus reducing related issues in the developed products. An experimental evaluation of the approach is conducted on a car driver monitoring use case. In addition, the advantages and disadvantages of the proposed approach as well as future work directions are indicated.

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“…For optimal utilization of the resources, a dynamic scheduling interface is also provided to sense the status of available resources and to offload divided tasks in a distributed way for computation efficiency. Safe-ShareRide is an edge-based attack detection system addressing in-vehicle security for ridesharing services [22]. Its three detection stages leverage both the smartphones of drivers and passengers as edge computing platform to collect multimedia information in vehicles.…”

Section: K Other Edge Computing Systemsmentioning

confidence: 99%

A Survey on Edge Computing Systems and Tools

Liu¹,

Tang²,

Li³

et al. 2019

Proc. IEEE

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224

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Driven by the visions of Internet of Things and 5G communications, the edge computing systems integrate computing, storage and network resources at the edge of the network to provide computing infrastructure, enabling developers to quickly develop and deploy edge applications. Nowadays the edge computing systems have received widespread attention in both industry and academia. To explore new research opportunities and assist users in selecting suitable edge computing systems for specific applications, this survey paper provides a comprehensive overview of the existing edge computing systems and introduces representative projects. A comparison of open source tools is presented according to their applicability. Finally, we highlight energy efficiency and deep learning optimization of edge computing systems. Open issues for analyzing and designing an edge computing system are also studied in this survey. F. Liu is with the

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SafeShareRide: Edge-Based Attack Detection in Ridesharing Services

Cited by 28 publications

References 24 publications

Edge Computing for Autonomous Driving: Opportunities and Challenges

Edge Computing for Autonomous Driving: Opportunities and Challenges

Security and Privacy Analysis of Smartphone-Based Driver Monitoring Systems from the Developer’s Point of View

A Survey on Edge Computing Systems and Tools

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