Security Analysis of Intelligent Transportation Systems Based on Simulation Data

Liang, Ming; Zhao, Gang; Huang, Minhuan; Kuang, Xiaohui; Li, Hu; Zhang, Ming

doi:10.1109/icdis.2018.00037

Cited by 9 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A cadeia do frio compreende o gerenciamento da temperatura e condições ideais de produtos perecíveis, no intuito de manter a qualidade e a segurança dos mesmos, desde a origem de sua produção, passando pela cadeia de distribuição até chegar ao consumidor final. Na sua essência, busca manter a temperatura adequada enquanto visa minimizar o custo durante o processo de entrega (Zhang, 2018). No caso de alimentos, possui como processos: colheita de alimentos, pré-resfriamento, processamento, transporte, armazenamento, distribuição, varejo e refrigeração doméstica, garantindo a qualidade e segurança dos alimentos (GCCA, 2016): portanto, garantindo que os produtos perecíveis sejam seguros e de alta qualidade no ponto de consumo.…”

Section: Cadeia Do Friounclassified

See 1 more Smart Citation

Engenharia de Produção: alicerce da competitividade

2021

Engenharia De Produção: Alicerce Da Competitividade

View full text Add to dashboard Cite

show abstract

Section: Cadeia Do Friounclassified

“…focado no custo de transporte, emissão de carbono e satisfação do cliente. Zhang (2018) Fonte: Autores ( 2021)…”

Section: Roteirizaçãounclassified

Engenharia de Produção: alicerce da competitividade

2021

Engenharia De Produção: Alicerce Da Competitividade

View full text Add to dashboard Cite

show abstract

“…The cyber-security research in transportation systems has three major goals: (1) to identify and to classify the existing threats and vulnerabilities immanent to cyber-physical transportation systems, (2) to develop risk assessment methodologies, and (3) to propose new technological and engineering countermeasures to the existing threats. Cyber-security in transportation systems is actively explored in at least two levels of granularity: Intra-vehicle level (e.g., in-vehicle communication between components via controller area network (CAN) buses [ 6 ]), and inter-vehicle level (e.g., communications between various components of a large-scale intelligent transportation system [ 7 , 8 ]).…”

Section: Related Workmentioning

confidence: 99%

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

Kashevnik

Ponomarev

Shilov

et al. 2020

Sensors

View full text Add to dashboard Cite

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.

show abstract

“…Modern vehicles in intelligent transport systems can communicate with each other, as well as roadside infrastructure units, in order to increase transport efficiency and road safety. For example, there are techniques to alert drivers in advance about traffic incidents and to help them avoid congestion, [7]. On the other hand, threats to such systems may limit the benefits of such technologies, as they rely on technological environment boundaries, poorly connected and mainly centralized information.…”

Section: Introductionmentioning

confidence: 99%

Big Data Analytics for Logistics and Distributions Using Blockchain

Petroni

Moraes

Gonçalves

2018

Advances in Production Management Systems. Smart Manufacturing for Industry 4.0

View full text Add to dashboard Cite

The volume of data generated is increasing. Companies capture large amounts of bytes of information about customers, vendors, products, sensory components, and especially their manufacturing operations. However, important problems, such as the Supply Chain Management processes, present difficulties regarding the security, integrity and validity of information generated in different databases. Blockchain technology presents itself as a disruptive process control technology where, through Smart Contracts, it provides transaction reliability and assures the parties involved that its purpose is strictly adhered to. Meanwhile, Big Data is offered as a solution to analyze all the information originated from the operations generated. In this article, as a contribution, possibilities of information generation will be presented, with the interaction of Blockchain, Cyber Physical Systems, Internet of Things technologies in function of the Supply Chain Management processes, so that they can later be analyzed through Big Data, giving users better controls and decision management in favor of improving their business.

show abstract

Security Analysis of Intelligent Transportation Systems Based on Simulation Data

Cited by 9 publications

References 4 publications

Engenharia de Produção: alicerce da competitividade

Engenharia de Produção: alicerce da competitividade

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

Big Data Analytics for Logistics and Distributions Using Blockchain

Contact Info

Product

Resources

About