TFMD-SDVN: a trust framework for misbehavior detection in the edge of software-defined vehicular network
- Publisher:
- SPRINGER
- Publication Type:
- Journal Article
- Citation:
- Journal of Supercomputing, 2022, 78, (6), pp. 7948-7981
- Issue Date:
- 2022-04-01
Closed Access
Filename | Description | Size | |||
---|---|---|---|---|---|
TFMD-SDVN a trust framework for misbehavior detection in the edge of software-defined vehicular network.pdf | Published version | 3.61 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
In this paper, a trust framework is proposed for misbehavior detection in software defined vehicular networks (TFMD-SDVN) to detect the correct events in the network reported by the trusted or untrusted nodes. The trust value of a node is calculated based on rating, recommendation, and similarity. If the trust value is greater than a threshold, then the event reported by the event reporting node (ERN) is assumed to be correct. The performance of the proposed work is evaluated using OMNeT++ network simulator and SUMO traffic simulator in Veins hybrid framework. The performance parameters taken are True Positive Rate (TPR), False Positive Rate (FPR), Detection Time (DT), and Packet Delivery Ratio (PDR). Simulation results show that the proposed approach performs better than ART scheme, RPRep scheme, and BYOR scheme.
Please use this identifier to cite or link to this item: