Conceptual Model for Connected Vehicles Safety and Security using Big Data Analytics
| Autor: | Muslihah Wook, Khairul Khalil, Noor Afiza Mat Razali, Nuraini Shamsaimon |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
050210 logistics & transportation
General Computer Science business.industry Event (computing) Computer science 05 social sciences Big data Conceptual model (computer science) Physical layer Vulnerability Network layer Computer security computer.software_genre Application layer 03 medical and health sciences 0302 clinical medicine Analytics TheoryofComputation_LOGICSANDMEANINGSOFPROGRAMS 0502 economics and business 030212 general & internal medicine business computer Implementation |
| Zdroj: | International Journal of Advanced Computer Science and Applications. 11 |
| ISSN: | 2156-5570 2158-107X |
| DOI: | 10.14569/ijacsa.2020.0111136 |
| Popis: | The capability of Connected Vehicles (CVs) connecting to a nearby vehicle, surrounding infrastructure and cyberspace presents a high risk in the aspect of safety and security of the CV and others. Data volume generated from the sensors and infrastructure in CVs environment are enormous. Thus, CVs implementations require a real-time big data processing and analytics to detect any anomaly in the CVs’s environment which are physical layer, network layer and application layer. CVs are exposed to various vulnerabilities associated with exploitations or malfunctions of the components in each layer that could result in various safety and security event such as congestion and collision. The safety and security risks added an extra layer of required protection for the CVs implementation that need to be studied and refined. To address this gap, this research aims to determine the basic components of safety and security for CVs implementation and propose a conceptual model for safety and security in CVs by applying the machine learning and deep learning techniques. The proposed model is highly correlated to safety and security and could be applied in congestion and collision prediction. |
| Databáze: | OpenAIRE |
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