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.

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: 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)

Beteiligte, Orts- und Zeitangaben

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

Weitere Informationen

• DOI: 10.1515/comp-2019-0022
• URN: urn:nbn:de:101:1-2410291753538.202771017243
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 15.08.2025, 07:25 MESZ