Trust-Distortion Resistant Trust Management Frameworks on Mobile Ad Hoc Networks: A Survey

Abstract : Trust management is a promising approach to conduct nodes' transactions and establish management interactions in distributed mobile ad hoc networks (MANETs) in which nodes' collaboration is critical to achieve system goals. Lack of centralized management, severe resource constraints (e.g., computing power, energy, and bandwidth), and important network dynamics (e.g., topology changes, node mobility, node failure, and propagation channel conditions) make trust management a challenging task in such a network. Mainly, some trust management basis may be exploited to fulfill new attacks. In this work, we present a holistic view on various trust management frameworks geared for MANETs, capable to handle main existing attacks deceiving trustworthiness computation to mislead trust-based network operations, referred to as trust-distortion attacks. Besides, we propose a taxonomy of main identified trust-distortion attacks based on how the trustworthiness estimation of a node about another node is distorted. Moreover, we provide a holistic classification of main evaluation metrics, which can be used to evaluate and compare such frameworks. For each framework, a unified approach is used to describe the trust model, taking each component required for trust management as a guideline. Moreover, each framework is analyzed regarding its resistance against different trust-distortion attacks, the framework unique features, merits, demerits, and findings. Finally, we compare different trust-distortion resistant frameworks and outline the open issues and future research directions.
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https://hal.sorbonne-universite.fr/hal-02099100
Contributor : Guy Pujolle <>
Submitted on : Saturday, April 13, 2019 - 9:17:02 PM
Last modification on : Wednesday, May 15, 2019 - 3:39:37 AM

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Zeinab Movahedi, Zahra Hosseini, Fahimeh Bayan, Guy Pujolle. Trust-Distortion Resistant Trust Management Frameworks on Mobile Ad Hoc Networks: A Survey. IEEE Communications Surveys & Tutorials, 2016, 18 (2), pp.1287-1309. ⟨10.1109/COMST.2015.2496147⟩. ⟨hal-02099100⟩

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