An intrusion detection system for connected vehicles in smart cities

Aloqaily, Moayad
Otoum, Safa
Ridhawi, Ismaeel Al
Jararweh, Yaser
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Elsevier B.V.
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.
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:
Connected vehicles, Intrusion detection, QoE, QoS, Service-specific clusters, Smart city, Smart transportation, Vehicular cloud computing, Decision trees, Distributed database systems, Learning systems, Quality of service, Reduction, Smart city, Trees (mathematics), Trusted computing, Vehicles, Classification technique, False positive and false negatives, Intrusion Detection Systems, Quality of experience (QoE), Service-specific clusters, Transportation system, Trusted third parties, Vehicular clouds, Intrusion detection
Aloqaily, M., Otoum, S., Ridhawi, I. A., & Jararweh, Y. (2019). An intrusion detection system for connected vehicles in smart cities. Ad Hoc Networks, 90.