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Open AccessArticle

Secure Intelligent Vehicular Network Using Fog Computing

Computer Science and Engineering Department, University of Bridgeport, Bridgeport, CT 06604, USA
Author to whom correspondence should be addressed.
Electronics 2019, 8(4), 455;
Received: 16 February 2019 / Revised: 16 April 2019 / Accepted: 18 April 2019 / Published: 24 April 2019
(This article belongs to the Special Issue Vehicular Networks and Communications)
VANET (vehicular ad hoc network) has a main objective to improve driver safety and traffic efficiency. The intermittent exchange of real-time safety message delivery in VANET has become an urgent concern due to DoS (denial of service) and smart and normal intrusions (SNI) attacks. The intermittent communication of VANET generates huge amount of data which requires typical storage and intelligence infrastructure. Fog computing (FC) plays an important role in storage, computation, and communication needs. In this research, fog computing (FC) integrates with hybrid optimization algorithms (OAs) including the Cuckoo search algorithm (CSA), firefly algorithm (FA), firefly neural network, and the key distribution establishment (KDE) for authenticating both the network level and the node level against all attacks for trustworthiness in VANET. The proposed scheme is termed “Secure Intelligent Vehicular Network using fog computing” (SIVNFC). A feedforward back propagation neural network (FFBP-NN), also termed the firefly neural, is used as a classifier to distinguish between the attacking vehicles and genuine vehicles. The SIVNFC scheme is compared with the Cuckoo, the FA, and the firefly neural network to evaluate the quality of services (QoS) parameters such as jitter and throughput. View Full-Text
Keywords: VANET; fog computing; DoS attacks and intrusion; FFBPNN; Cuckoo search algorithm; firefly algorithm; QoS parameters VANET; fog computing; DoS attacks and intrusion; FFBPNN; Cuckoo search algorithm; firefly algorithm; QoS parameters
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Erskine, S.K.; Elleithy, K.M. Secure Intelligent Vehicular Network Using Fog Computing. Electronics 2019, 8, 455.

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