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Secure Incident & Evidence Management Framework (SIEMF) for Internet of Vehicles using Deep Learning and Blockchain
Philip A.O.,
Published in Walter de Gruyter GmbH
Volume: 10
Issue: 1
Pages: 408 - 421
Even though there is continuous improvement in road and vehicle safety, road traffic incidents have been increasing over last few decades. There is a need to reduce traffic incidents like accidents through predictive analysis and timely warnings while at the same time data related to accidents and traffic violations need to be maintained in a tamper proof storage system that can be retrieved for forensic analysis and law enforcement at a later stage. The Secure Incident and Evidence Management Framework (SIEMF) proposed in this work address these two challenges of predictive modeling for timely warning and secure evidence management for forensics analysis in case of accidents and traffic violations. The system proposes a deep learning based predictive incident modeling with blockchain and CP-ABE based access control for the incident data stored in blockchain. © 2020 A. Oommen Philip et al., published by De Gruyter 2020.
About the journal
JournalData powered by TypesetOpen Computer Science
PublisherData powered by TypesetWalter de Gruyter GmbH
Open AccessNo