Anomaly Detection for Internet of Vehicles: A Trust Management Scheme with Affinity Propagation
Joint Authors
Wang, Shangguang
Yang, Fangchun
Yang, Shu
Liu, Zhihan
Li, Jinglin
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-03-24
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Telecommunications Engineering
Abstract EN
Anomaly detection is critical for intelligent vehicle (IV) collaboration.
Forming clusters/platoons, IVs can work together to accomplish complex jobs that they are unable to perform individually.
To improve security and efficiency of Internet of Vehicles, IVs’ anomaly detection has been extensively studied and a number of trust-based approaches have been proposed.
However, most of these proposals either pay little attention to leader-based detection algorithm or ignore the utility of networked Roadside-Units (RSUs).
In this paper, we introduce a trust-based anomaly detection scheme for IVs, where some malicious or incapable vehicles are existing on roads.
The proposed scheme works by allowing IVs to detect abnormal vehicles, communicate with each other, and finally converge to some trustworthy cluster heads (CHs).
Periodically, the CHs take responsibility for intracluster trust management.
Moreover, the scheme is enhanced with a distributed supervising mechanism and a central reputation arbitrator to assure robustness and fairness in detecting process.
The simulation results show that our scheme can achieve a low detection failure rate below 1%, demonstrating its ability to detect and filter the abnormal vehicles.
American Psychological Association (APA)
Yang, Shu& Liu, Zhihan& Li, Jinglin& Wang, Shangguang& Yang, Fangchun. 2016. Anomaly Detection for Internet of Vehicles: A Trust Management Scheme with Affinity Propagation. Mobile Information Systems،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1111540
Modern Language Association (MLA)
Yang, Shu…[et al.]. Anomaly Detection for Internet of Vehicles: A Trust Management Scheme with Affinity Propagation. Mobile Information Systems No. 2016 (2016), pp.1-10.
https://search.emarefa.net/detail/BIM-1111540
American Medical Association (AMA)
Yang, Shu& Liu, Zhihan& Li, Jinglin& Wang, Shangguang& Yang, Fangchun. Anomaly Detection for Internet of Vehicles: A Trust Management Scheme with Affinity Propagation. Mobile Information Systems. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1111540
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1111540