Secure Incident & Evidence Management Framework (SIEMF) for Internet of Vehicles using Deep Learning and Blockchain

Abstract: 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.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Secure Incident & Evidence Management Framework (SIEMF) for Internet of Vehicles using Deep Learning and Blockchain ; volume:10 ; number:1 ; year:2020 ; pages:408-421 ; extent:14 ; Open computer science ; 10, Heft 1 (2020), 408-421 (gesamt 14)

Contributors, Places and Time

• Creator: Philip, A. Oommen ; Saravanaguru, RA K.

Further information

• DOI: 10.1515/comp-2019-0022
• URN: urn:nbn:de:101:1-2410291753538.202771017243
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:25 AM CEST