PROVEST: Provenance-based Trust Model for Delay Tolerant Networks

PROVEST: Provenance-based Trust Model for Delay Tolerant Networks

ABSTRACT:

Delay tolerant networks (DTNs) are often encountered in military network environments where end-to-end connectivity is not guaranteed due to frequent disconnection or delay. This work proposes a provenance-based trust framework, namely PROVEST (PROVEnance-baSed Trust model) that aims to achieve accurate peer-to-peer trust assessment and maximize the delivery of correct messages received by destination nodes while minimizing message delay and communication cost under resource-constrained network environments. Provenance refers to the history of ownership of a valued object or information. We leverage the interdependency between trustworthiness of information source and information itself in PROVEST. PROVEST takes a data-driven approach to reduce resource consumption in the presence of selfish or malicious nodes while estimating a node’s trust dynamically in response to changes in the environmental and node conditions. This work adopts a model-based method to evaluate the performance of PROVEST (i.e., trust accuracy and routing performance) using Stochastic Petri Nets. We conduct a comparative performance analysis of PROVEST against existing trust-based and non-trust-based DTN routing protocols to analyze the benefits of PROVEST. We validate PROVEST using a real dataset of DTN mobility traces.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows XP/UBUNTU.
  • Implementation : NS2
  • NS2 Version : 2.28
  • Front End : OTCL (Object Oriented Tool Command  Language)
  • Tool : Cygwin (To simulate in Windows OS)

REFERENCE:

Jin-Hee Cho, Senior Member, IEEE, Ing-Ray Chen, Member, IEEE, “PROVEST: Provenance-based Trust Model for Delay Tolerant Networks”, IEEE Transactions on Dependable and Secure Computing, 2018.

 

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