An intrusion detection system for connected vehicles in smart cities

dc.contributor.author Aloqaily, Moayad
dc.contributor.author Otoum, Safa
dc.contributor.author Ridhawi, Ismaeel Al
dc.contributor.author Jararweh, Yaser
dc.date.accessioned 2021-03-24T06:38:52Z
dc.date.available 2021-03-24T06:38:52Z
dc.date.copyright © 2019
dc.date.issued 2019-07
dc.description This article is not available at CUD collection. The version of scholarly record of this article is published in Ad Hoc Networks (2019), available online at: https://doi.org/10.1016/j.adhoc.2019.02.001 en_US
dc.description.abstract In the very near future, transportation will go through a transitional period that will shape the industry beyond recognition. Smart vehicles have played a significant role in the advancement of intelligent and connected transportation systems. Continuous vehicular cloud service availability in smart cities is becoming a crucial subscriber necessity which requires improvement in the vehicular service management architecture. Moreover, as smart cities continue to deploy diversified technologies to achieve assorted and high-performance cloud services, security issues with regards to communicating entities which share personal requester information still prevails. To mitigate these concerns, we introduce an automated secure continuous cloud service availability framework for smart connected vehicles that enables an intrusion detection mechanism against security attacks and provides services that meet users’ quality of service (QoS) and quality of experience (QoE) requirements. Continuous service availability is achieved by clustering smart vehicles into service-specific clusters. Cluster heads are selected for communication purposes with trusted third-party entities (TTPs) acting as mediators between service requesters and providers. The most optimal services are then delivered from the selected service providers to the requesters. Furthermore, intrusion detection is accomplished through a three-phase data traffic analysis, reduction, and classification technique used to identify positive trusted service requests against false requests that may occur during intrusion attacks. The solution adopts deep belief and decision tree machine learning mechanisms used for data reduction and classification purposes, respectively. The framework is validated through simulations to demonstrate the effectiveness of the solution in terms of intrusion attack detection. The proposed solution achieved an overall accuracy of 99.43% with 99.92% detection rate and 0.96% false positive and false negative rate of 1.53%. © 2019 Elsevier B.V. en_US
dc.identifier.citation Aloqaily, M., Otoum, S., Ridhawi, I. A., & Jararweh, Y. (2019). An intrusion detection system for connected vehicles in smart cities. Ad Hoc Networks, 90. https://doi.org/10.1016/j.adhoc.2019.02.001 en_US
dc.identifier.issn 15708705
dc.identifier.uri http://dx.doi.org/10.1016/j.adhoc.2019.02.001
dc.identifier.uri http://hdl.handle.net/20.500.12519/356
dc.language.iso en en_US
dc.publisher Elsevier B.V. en_US
dc.relation Authors Affiliations : Aloqaily, M., Canadian University Dubai, 1st Interchange, Sheikh Zayed Rd., Dubai, United Arab Emirates; Otoum, S., University of Ottawa, School of Electrical Engineering and Computer Science, Ottawa, ON K1N6N5, Canada; Ridhawi, I.A., University of Ottawa, School of Electrical Engineering and Computer Science, Ottawa, ON K1N6N5, Canada; Jararweh, Y., Jordan University of Science and Technology (JUST), Irbid, Jordan
dc.relation.ispartofseries Ad Hoc Networks;Vol. 90
dc.rights License to reuse the abstract has been secured from Elsevier and Copyright Clearance Center.
dc.rights.holder Copyright : © 2019 Elsevier B.V.
dc.rights.uri https://s100.copyright.com/CustomerAdmin/PLF.jsp?ref=246594bb-663c-464b-8ecd-e672e6f33a65
dc.subject Connected vehicles en_US
dc.subject Intrusion detection en_US
dc.subject QoE en_US
dc.subject QoS en_US
dc.subject Service-specific clusters en_US
dc.subject Smart city en_US
dc.subject Smart transportation en_US
dc.subject Vehicular cloud computing en_US
dc.subject Decision trees en_US
dc.subject Distributed database systems en_US
dc.subject Learning systems en_US
dc.subject Quality of service en_US
dc.subject Reduction en_US
dc.subject Smart city en_US
dc.subject Trees (mathematics) en_US
dc.subject Trusted computing en_US
dc.subject Vehicles en_US
dc.subject Classification technique en_US
dc.subject False positive and false negatives en_US
dc.subject Intrusion Detection Systems en_US
dc.subject Quality of experience (QoE) en_US
dc.subject Service-specific clusters en_US
dc.subject Transportation system en_US
dc.subject Trusted third parties en_US
dc.subject Vehicular clouds en_US
dc.subject Intrusion detection en_US
dc.title An intrusion detection system for connected vehicles in smart cities en_US
dc.type Article en_US
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