TFMD-SDVN: a trust framework for misbehavior detection in the edge of software-defined vehicular network

Rajendra Prasad Nayak, Srinivas Sethi, Sourav Kumar Bhoi, Debasis Mohapatra, Rashmi Ranjan Sahoo, Pradip Kumar Sharma, Deepak Puthal

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

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.

Original languageBritish English
Pages (from-to)7948-7981
Number of pages34
JournalJournal of Supercomputing
Volume78
Issue number6
DOIs
StatePublished - Apr 2022

Keywords

  • Event validation
  • Recommendation
  • SDVN
  • Trust
  • Trusted
  • Untrusted

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