Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
Resiliency and availability in community and public service networks may be economically enhanced by building new ad hoc networks of private mobile devices and joining these to public service networks at specific trusted points. Resiliency in such ad hoc networks relies on the afforded increased availability but also on security which is in turn built on trust. In this article, we describe MACH-T, a novel behavior-based algorithm for mobile ad hoc network node trust building. MACH-T uses historical mobile node geographic location traces to incrementally calculate node trust values based on the concepts of node capability, commitment, and consistency. We describe experiments and results from evaluating MACH-T using real GPS traces from the Microsoft Research Geolife and University of Rome Tor Vergata Roma Taxi datasets. Our results show that MACH-T builds a reliable trust value and corresponding confidence value based on learned patterns of time spent in qualifying geographic locations.
Recommended Citation
Thurston, Karen and Conte De Leon, Daniel, "MACH-T: A Behavior-based Mobile Node Trust Evaluation Algorithm" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 7.
https://aisel.aisnet.org/hicss-56/li/research/7
MACH-T: A Behavior-based Mobile Node Trust Evaluation Algorithm
Online
Resiliency and availability in community and public service networks may be economically enhanced by building new ad hoc networks of private mobile devices and joining these to public service networks at specific trusted points. Resiliency in such ad hoc networks relies on the afforded increased availability but also on security which is in turn built on trust. In this article, we describe MACH-T, a novel behavior-based algorithm for mobile ad hoc network node trust building. MACH-T uses historical mobile node geographic location traces to incrementally calculate node trust values based on the concepts of node capability, commitment, and consistency. We describe experiments and results from evaluating MACH-T using real GPS traces from the Microsoft Research Geolife and University of Rome Tor Vergata Roma Taxi datasets. Our results show that MACH-T builds a reliable trust value and corresponding confidence value based on learned patterns of time spent in qualifying geographic locations.
https://aisel.aisnet.org/hicss-56/li/research/7