An intrusion detection system for connected vehicles in smart cities

By Moayad Aloqaily, Safa Otoum, I. A. Ridhawi, Y. Jararweh. Ad hoc networks, 2019

366 citations recorded by the source corpus.

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%.

Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/94554103c275907d052fc87598c032ca041a74c9

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Citations in the corpus

A Review on Digital Twin Technology in Smart Grid, Transportation System and Smart City: Challenges and Future (2023, methodology): “A hybrid model combined CNN and LSTM algorithms are proposed in [70], where the model was used for electricity theft detection.”
Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study (2020, neutral): “For securing the connectivity aspect of connected vehicles, Aloqaily et al. [57] proposed an intrusion detection system, named D2H-IDS, which is based on a deep belief network and decision tree.”
MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles (2021, methodology): “Aloqaily et al. [23] proposed a network IDS for IoV and connected vehicles using deep belief network (DBN) and decision tree (DT) algorithms.”
Blockchain Technology in Healthcare: A Comprehensive Review and Directions for Future Research (2019, background): “[153] introduced an intrusion detection mechanism to resist attack such as Denial of Service, Probe, Remote to user, etc.”